From 49ff6b5199b40104f3ef7eeb116af78868b8ba1c Mon Sep 17 00:00:00 2001
From: MinSeok Kang <rkdals1148@gmail.com>
Date: Thu, 2 Oct 2025 23:47:22 +0800
Subject: [PATCH 1/6] feat: update propensity_score_and_dml notebook (restored
 from checkpoint)

---
 book/ate/propensity_score_and_dml.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/book/ate/propensity_score_and_dml.ipynb b/book/ate/propensity_score_and_dml.ipynb
index c2e7639..f0310ee 100644
--- a/book/ate/propensity_score_and_dml.ipynb
+++ b/book/ate/propensity_score_and_dml.ipynb
@@ -33,7 +33,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -47,9 +47,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From a6bffba7aed89ed8051747b784e01b5f38604527 Mon Sep 17 00:00:00 2001
From: MinSeok Kang <rkdals1148@gmail.com>
Date: Thu, 2 Oct 2025 23:53:33 +0800
Subject: [PATCH 2/6] fix: replace notebook with full version from 3dd2651

---
 book/ate/propensity_score_and_dml.ipynb | 2555 ++++++++++++++++++++++-
 1 file changed, 2549 insertions(+), 6 deletions(-)

diff --git a/book/ate/propensity_score_and_dml.ipynb b/book/ate/propensity_score_and_dml.ipynb
index f0310ee..07f6103 100644
--- a/book/ate/propensity_score_and_dml.ipynb
+++ b/book/ate/propensity_score_and_dml.ipynb
@@ -11,9 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- Matching\n",
-    "- IPW, AIPW, Doubly Robust Estimator\n",
-    "- Double Machine Learning (비모수 버전의 Regression 처럼 활용 가능)"
+    "- Matching"
    ]
   },
   {
@@ -29,13 +27,2558 @@
     "        async>\n",
     "</script>"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- IPW, AIPW, Doubly Robust Estimator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "출처: https://matheusfacure.github.io/python-causality-handbook/11-Propensity-Score.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from causalinference import CausalModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>schoolid</th>\n",
+       "      <th>intervention</th>\n",
+       "      <th>achievement_score</th>\n",
+       "      <th>success_expect</th>\n",
+       "      <th>ethnicity</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>frst_in_family</th>\n",
+       "      <th>school_urbanicity</th>\n",
+       "      <th>school_mindset</th>\n",
+       "      <th>school_achievement</th>\n",
+       "      <th>school_ethnic_minority</th>\n",
+       "      <th>school_poverty</th>\n",
+       "      <th>school_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>73</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.480828</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.462945</td>\n",
+       "      <td>0.652608</td>\n",
+       "      <td>-0.515202</td>\n",
+       "      <td>-0.169849</td>\n",
+       "      <td>0.173954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3435</th>\n",
+       "      <td>76</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.987277</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.334544</td>\n",
+       "      <td>0.648586</td>\n",
+       "      <td>-1.310927</td>\n",
+       "      <td>0.224077</td>\n",
+       "      <td>-0.426757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9963</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-0.152340</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-2.289636</td>\n",
+       "      <td>0.190797</td>\n",
+       "      <td>0.875012</td>\n",
+       "      <td>-0.724801</td>\n",
+       "      <td>0.761781</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4488</th>\n",
+       "      <td>67</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.358336</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>-1.115337</td>\n",
+       "      <td>1.053089</td>\n",
+       "      <td>0.315755</td>\n",
+       "      <td>0.054586</td>\n",
+       "      <td>1.862187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2637</th>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.360920</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.538975</td>\n",
+       "      <td>1.433826</td>\n",
+       "      <td>-0.033161</td>\n",
+       "      <td>-0.982274</td>\n",
+       "      <td>1.591641</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      schoolid  intervention  achievement_score  success_expect  ethnicity  \\\n",
+       "259         73             1           1.480828               5          1   \n",
+       "3435        76             0          -0.987277               5         13   \n",
+       "9963         4             0          -0.152340               5          2   \n",
+       "4488        67             0           0.358336               6         14   \n",
+       "2637        16             1           1.360920               6          4   \n",
+       "\n",
+       "      gender  frst_in_family  school_urbanicity  school_mindset  \\\n",
+       "259        2               0                  1       -0.462945   \n",
+       "3435       1               1                  4        0.334544   \n",
+       "9963       2               1                  0       -2.289636   \n",
+       "4488       1               0                  4       -1.115337   \n",
+       "2637       1               0                  1       -0.538975   \n",
+       "\n",
+       "      school_achievement  school_ethnic_minority  school_poverty  school_size  \n",
+       "259             0.652608               -0.515202       -0.169849     0.173954  \n",
+       "3435            0.648586               -1.310927        0.224077    -0.426757  \n",
+       "9963            0.190797                0.875012       -0.724801     0.761781  \n",
+       "4488            1.053089                0.315755        0.054586     1.862187  \n",
+       "2637            1.433826               -0.033161       -0.982274     1.591641  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"./data/learning_mindset.csv\")\n",
+    "data.sample(5, random_state=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Propensity Score 계산"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "categ = [\"ethnicity\", \"gender\", \"school_urbanicity\"]\n",
+    "cont = [\"school_mindset\", \"school_achievement\", \"school_ethnic_minority\", \"school_poverty\", \"school_size\"]\n",
+    "\n",
+    "data_with_categ = pd.concat([\n",
+    "    data.drop(columns=categ), \n",
+    "    pd.get_dummies(data[categ], columns=categ, drop_first=False)\n",
+    "], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "T = 'intervention'\n",
+    "Y = 'achievement_score'\n",
+    "X = data_with_categ.columns.drop(['schoolid', T, Y])\n",
+    "\n",
+    "ps_model = LogisticRegression(C=1e6).fit(data_with_categ[X], data_with_categ[T])\n",
+    "\n",
+    "data_ps = data.assign(propensity_score=ps_model.predict_proba(data_with_categ[X])[:, 1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## IPW 생성 및 ATE 추정"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.linear_model import LogisticRegression, Ridge\n",
+    "from causallib.estimation.standardization import Standardization\n",
+    "from causallib.estimation.ipw import IPW\n",
+    "from causallib.estimation.doubly_robust import AIPW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vanilla IPW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original Sample Size 10391\n",
+      "Treated Population Sample Size 10387.611484606225\n",
+      "Untreated Population Sample Size 10391.506082909109\n"
+     ]
+    }
+   ],
+   "source": [
+    "weight_t = 1/data_ps.query(\"intervention==1\")[\"propensity_score\"]\n",
+    "weight_nt = 1/(1-data_ps.query(\"intervention==0\")[\"propensity_score\"])\n",
+    "print(\"Original Sample Size\", data.shape[0])\n",
+    "print(\"Treated Population Sample Size\", sum(weight_t))\n",
+    "print(\"Untreated Population Sample Size\", sum(weight_nt))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Y1: 0.25981028516541704\n",
+      "Y0: -0.12903052853557984\n",
+      "ATE 0.38884081370099727\n"
+     ]
+    }
+   ],
+   "source": [
+    "weight = ((data_ps[\"intervention\"]-data_ps[\"propensity_score\"]) /\n",
+    "          (data_ps[\"propensity_score\"]*(1-data_ps[\"propensity_score\"])))\n",
+    "\n",
+    "y1_ipw = sum(data_ps.query(\"intervention==1\")[\"achievement_score\"]*weight_t) / len(data)\n",
+    "y0_ipw = sum(data_ps.query(\"intervention==0\")[\"achievement_score\"]*weight_nt) / len(data)\n",
+    "\n",
+    "ate_ipw = y1_ipw - y0_ipw\n",
+    "#ate = np.mean(weight * data_ps[\"achievement_score\"])\n",
+    "\n",
+    "print(\"Y1:\", y1_ipw)\n",
+    "print(\"Y0:\", y0_ipw)\n",
+    "print(\"ATE\", np.mean(weight * data_ps[\"achievement_score\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "결과 해석: \n",
+    "1. Treatment 받은 개인이 Treatment 받지 않은 동료보다 achievement_score가 0.38 표준편차 더 크다. (achievement_score는 표준화된 결과이기 때문에 표준 편차의 차이로 해석)\n",
+    "2. 아무도 Treatment 받지 않은 경우 일반적인 성취 수준이 현재보다 0.12 표준편차 더 낮다.\n",
+    "3. 모든 사람이 Treatment(세미나)를 받았다면 일반적인 성취 수준이 0.25 표준편차 더 높음."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "또한 ate를 나타내는 하나의 코드가 더 있다.     \n",
+    "위의 코드에 주석처리한 부분을 그대로 실행해보면 똑같은 결과를 얻을 수 있는 것을 알 수 있다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.38884081370099727\n"
+     ]
+    }
+   ],
+   "source": [
+    "ate_ipw = np.mean(weight * data_ps[\"achievement_score\"])\n",
+    "print(ate_ipw)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "두 결과값이 같은 이유는 Matheus Facure(출처)의 책에서 자세히 설명되어 있다.    \n",
+    "(참고: $ \\mathrm{ATE}=\\mathbb{E}\\!\\left[\\, Y\\,\\dfrac{T-e(X)}{e(X)\\,\\bigl(1-e(X)\\bigr)} \\right] $)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Doubly Robust Estimator & AIPW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "출처: https://causallib.readthedocs.io/en/latest/causallib.estimation.doubly_robust.html?highlight=doubly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from causallib.estimation.ipw import IPW\n",
+    "from causallib.estimation.doubly_robust import AIPW\n",
+    "from causallib.estimation.standardization import Standardization\n",
+    "from sklearn.linear_model import LogisticRegression, LinearRegression, Ridge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = data[\"achievement_score\"]\n",
+    "T = data[\"intervention\"]\n",
+    "X = pd.get_dummies(\n",
+    "    data[[\"school_mindset\",\"school_achievement\",\"school_ethnic_minority\",\n",
+    "          \"school_poverty\",\"school_size\",\"ethnicity\",\"gender\",\"school_urbanicity\"]],\n",
+    "    drop_first=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "DR Estimator는 결과모형과 IPW값이 모두 필요함   \n",
+    "- Y값(achievement_score)을 Ridge로 예측(L2 패널티 부여)   \n",
+    "- IPW: 로지스틱 회귀 사용"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outcome_model = Standardization(learner=Ridge(alpha=1.0))\n",
+    "weight_model  = IPW(learner=LogisticRegression(max_iter=1000),\n",
+    "                    clip_min=0.01, clip_max=0.99, use_stabilized=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PS Score를 구할 때 max_iter을 충분히 큰 숫자(1000)으로 설정해 수치 최적화가 수렴할 수 있도록 설정합니다.   \n",
+    "또한 클리핑을 사용하여 $ \\hat{e} $가 [0.01, 0.99]에서만 존재하도록 극단 가중치를 완화합니다.  \n",
+    "(use_stabilized = True)   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "위에서 구한 PS score과 IPW를 활용하여 AIPW를 구합니다. (Vanilla)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "                learner=Ridge()), overlap_weighting=False, predict_proba=False, weight_covariates=None,\n",
+       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000)))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>AIPW</div></div><div><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('outcome_model',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">outcome_model&nbsp;</td>\n",
+       "            <td class=\"value\">Standardizati...arner=Ridge())</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weight_model',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weight_model&nbsp;</td>\n",
+       "            <td class=\"value\">IPW(_doc_link...ax_iter=1000))</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('outcome_covariates',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">outcome_covariates&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weight_covariates',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weight_covariates&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('overlap_weighting',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">overlap_weighting&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>outcome_model: Standardization</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__\"><pre>Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "                learner=Ridge())</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>learner: Ridge</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__learner__\"><pre>Ridge()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Ridge</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__learner__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">alpha&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">solver&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>weight_model: IPW</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__\"><pre>IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000))</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>learner: LogisticRegression</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__learner__\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__learner__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">penalty&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">dual&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">solver&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">1000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('multi_class',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">multi_class&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "                learner=Ridge()), overlap_weighting=False, predict_proba=False, weight_covariates=None,\n",
+       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000)))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dr = AIPW(outcome_model=outcome_model, weight_model=weight_model, overlap_weighting=False)\n",
+    "dr.fit(X, T, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "μ1 (A=1): 0.3032993079280466\n",
+      "μ0 (A=0): -0.14711303868200887\n",
+      "ATE (DR, vanilla): 0.45041234661005547\n"
+     ]
+    }
+   ],
+   "source": [
+    "pop_outcomes = dr.estimate_population_outcome(X, T, Y)\n",
+    "mu1_aipw, mu0_aipw = pop_outcomes[1], pop_outcomes[0]\n",
+    "ate_aipw = dr.estimate_effect(mu1_aipw, mu0_aipw, agg=\"population\")[\"diff\"]\n",
+    "print(\"μ1 (A=1):\", mu1_aipw)\n",
+    "print(\"μ0 (A=0):\", mu0_aipw)\n",
+    "print(\"ATE (DR, vanilla):\", ate_aipw)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "결과 해석은 IPW에서와 마찬가지로 생각하면 됩니다.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "위에서 구한 AIPW는 Propensity Score의 Overlap이 충분히 확보되었을 때는 좋은 결과를 나타냅니다.   \n",
+    "하지만 Overlap 구간이 불안정할 때는 Overlap-weighting = True이라는 기능을 활용해도 좋습니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "                learner=Ridge()), overlap_weighting=True, predict_proba=False, weight_covariates=None,\n",
+       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000)))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>AIPW</div></div><div><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('outcome_model',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">outcome_model&nbsp;</td>\n",
+       "            <td class=\"value\">Standardizati...arner=Ridge())</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weight_model',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weight_model&nbsp;</td>\n",
+       "            <td class=\"value\">IPW(_doc_link...ax_iter=1000))</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('outcome_covariates',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">outcome_covariates&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('weight_covariates',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">weight_covariates&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('overlap_weighting',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">overlap_weighting&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>outcome_model: Standardization</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__\"><pre>Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "                learner=Ridge())</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>learner: Ridge</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__learner__\"><pre>Ridge()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Ridge</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__learner__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">alpha&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">copy_X&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">solver&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">positive&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>weight_model: IPW</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__\"><pre>IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000))</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>learner: LogisticRegression</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__learner__\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__learner__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">penalty&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">dual&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">tol&nbsp;</td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">C&nbsp;</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">class_weight&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('solver',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">solver&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_iter&nbsp;</td>\n",
+       "            <td class=\"value\">1000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('multi_class',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">multi_class&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">verbose&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">warm_start&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('l1_ratio',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "                learner=Ridge()), overlap_weighting=True, predict_proba=False, weight_covariates=None,\n",
+       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000)))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dr_overlap = AIPW(outcome_model=outcome_model,\n",
+    "                  weight_model=weight_model,\n",
+    "                  overlap_weighting=True)\n",
+    "dr_overlap.fit(X, T, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ATE(DR, overlap-weighting): 0.34674117821712797\n"
+     ]
+    }
+   ],
+   "source": [
+    "pop_outcomes_ov = dr_overlap.estimate_population_outcome(X, T, Y)\n",
+    "ate_ov = dr_overlap.estimate_effect(pop_outcomes_ov[1], pop_outcomes_ov[0],\n",
+    "                                    agg=\"population\")[\"diff\"]\n",
+    "print(\" ATE(DR, overlap-weighting):\", ate_ov)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        estimator        μ1        μ0       ATE\n",
+      "0    IPW (manual)  0.259810 -0.129031  0.388841\n",
+      "1  AIPW (vanilla)  0.303299 -0.147113  0.450412\n",
+      "2  AIPW (overlap)       NaN       NaN  0.346741\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = pd.DataFrame([\n",
+    "    [\"IPW (manual)\",      y1_ipw,    y0_ipw,    ate_ipw],\n",
+    "    [\"AIPW (vanilla)\",    mu1_aipw,  mu0_aipw,  ate_aipw],\n",
+    "    [\"AIPW (overlap)\",    np.nan,    np.nan,    ate_ov]\n",
+    "], columns=[\"estimator\", \"μ1\", \"μ0\", \"ATE\"])\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "위에서 사용한 방법에 따라 ATE 값이 다르게 나타나는 것을 알 수 있습니다.   \n",
+    "  \n",
+    "하지만 Overlap-Weighting 옵션을 사용한 경우에는 ATE가 아닌 ATO를 추정한 것이고 둘을 직접 비교하는 것은 맞지 않을 수 있습니다.   \n",
+    "  \n",
+    "분석의 목적이 ATE를 추정하는 것인지 ATO를 추정하는 것인지 확인한 후 적절히 사용하면 됩니다.   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## IPW Robustness Check의 흐름\n",
+    "\n",
+    "각각의 수치에 대한 검증은 필수입니다.  \n",
+    "어떤 수치가 더 Robust하게 추정이 된 걸까요?   \n",
+    "\n",
+    "먼저 IPW로 구한 ATE의 신빙성을 테스트 해보겠습니다."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "출처: https://causallib.readthedocs.io/en/latest/causallib.evaluation.plots.plots.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from causallib.evaluation.plots.plots import plot_propensity_score_distribution\n",
+    "from causallib.evaluation import evaluate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### IPW 가중 후 |SMD| 변화 확인"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Absolute Standard Mean Difference', ylabel='Covariates'>"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "res.plot_covariate_balance(kind=\"love\", phase=\"valid\", thresh=0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Weighted이후에 |SMD|가 더 줄어든 것을 보았을 때, Weighted 이후 밸런스가 개선되었음을 확인할 수 있습니다."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### IPW 가중치 분포 및 유효표본수(ESS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ESS: 10354.681665498   (N: 10391 )\n"
+     ]
+    }
+   ],
+   "source": [
+    "w = weight_model.compute_weights(X, T)\n",
+    "ESS = (w.sum()**2) / (w**2).sum()\n",
+    "print(\"ESS:\", float(ESS), \"  (N:\", len(w), \")\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'IPW weights (log y)')"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(); plt.hist(w, bins=40); plt.yscale('log'); plt.title('IPW weights (log y)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) ESS와 N수가 거의 비슷한 것을 확인할 수 있음 -> 가중치가 고르게 퍼져있다\n",
+    "2) 히스토그램이 1 주변에 모여있고(= 가중치 과도하게 쏠리지 않음), 꼬리 부분도 완만(= 극단 가중치 거의 없음)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Propensity Score Overlap\n",
+    "아래는 처음에 Logistic Regression으로 추정한 Propensity Score의 Distribution을 나타낸 것입니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Propensity Distribution'}, xlabel='Propensity', ylabel='Probability density'>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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DAAAsjWQGAABYWqlfMwMAcL+cnBxdunSppMOAhfn6+srHx8ctxyKZAQA4zRij9PR0nT59uqRDQSlQoUIFVatW7ZrvA0cyAwBwWl4iU6VKFQUGBnIzUhSJMUbnz5/XsWPHJEkRERHXdDySGQCAU3JycuyJTFhYWEmHA4sLCAiQJB07dkxVqlS5piknFgADAJySt0YmMDCwhCNBaZH3t3St669IZgAALmFqCe7irr8lkhkAAGBpJDMAAMDSWAAMALgm01btKdbzDe9Y36X67dq1U4sWLTR9+nTPBHSZxMREnT59WkuXLi2W811NSkqKhg0bViyX0//www964okn9N1336lSpUoaMGCAxo4d6/GpSZIZAMB1zxijnJwclS3Lz2JRZWZmqmPHjkpISNDmzZu1Z88eJSYmqnz58nr66ac9em6mmQAApVZiYqLWrl2rV199VTabTTabTQcPHlRqaqpsNptWrFih2NhY+fv7a926dTLGaMqUKapTp44CAgLUvHlzffTRR/bj5eTkqG/fvoqOjlZAQIAaNGigV1991b49KSlJ8+fP16effmo/X2pqqg4ePCibzaZFixbptttuU0BAgG6++Wbt2bNHmzdvVmxsrIKCgtSlSxcdP37coQ3z5s1Tw4YNVa5cOcXExGjWrFn2bXnHXbJkiRISEhQYGKjmzZtrw4YNkqTU1FT16dNHGRkZ9niSkpI88l2/9957unDhglJSUtSkSRPdf//9Gj16tF555RUZYzxyzjwkMwCAUuvVV19VXFyc+vfvr6NHj+ro0aOKjIy0b3/mmWeUnJysXbt2qVmzZnruuec0b948zZ49Wz/++KOGDx+uhx9+WGvXrpUk5ebmqmbNmlq0aJF27typcePGafTo0Vq0aJEkacSIEerevbu6dOliP1+bNm3s5xs/fryee+45ff/99ypbtqx69uypZ555Rq+++qrWrVunffv2ady4cfb6c+fO1ZgxY/TCCy9o165dmjx5ssaOHav58+c7tHPMmDEaMWKEtm/frvr166tnz57Kzs5WmzZtNH36dIWEhNjjGTFiRIHf1bp16xQUFHTF1+TJkwv9rjds2KD4+Hj5+/vbyzp37qwjR47o4MGDzndaETCeBsCrFLb+wtV1EoAkhYaGys/PT4GBgapWrVq+7RMnTlTHjh0lSefOndMrr7yir776SnFxcZKkOnXq6JtvvtGbb76p+Ph4+fr6asKECfb9o6OjtX79ei1atEjdu3dXUFCQAgIClJWVVeD5RowYoc6dO0uShg4dqp49e2r16tVq27atJKlv375KSUmx1580aZJefvll3X///fbz7dy5U2+++aZ69+7tcNxu3bpJkiZMmKDGjRtr7969iomJUWhoqGw2W4Hx/FFsbKy2b99+xTqVKlUqdFt6erpq167tUFa1alX7tujo6Cse+1qQzAAArluxsbH29zt37tSFCxfsyU2eixcvqmXLlvbPb7zxht566y0dOnRIv/32my5evKgWLVo4db5mzZrZ3+f90Ddt2tShLO8W/8ePH9fhw4fVt29f9e/f314nOztboaGhhR4379EAx44dU0xMjFNxSb/fkbdevXpO1y/I5Qt986aXWAAMAICHlC9f3v4+NzdXkrRs2TLVqFHDoV7e1MmiRYs0fPhwvfzyy4qLi1NwcLBeeuklbdq0yanz+fr62t/n/cBfXpYXR97/zp07V61atXI4zuW3/i/ouHn7O2vdunXq2rXrFeuMHj1ao0ePLnBbtWrVlJ6e7lCWl5jlJW6eQjIDACjV/Pz8lJOTc9V6jRo1kr+/v9LS0hQfH19gnXXr1qlNmzYaNGiQvWzfvn1FOt/VVK1aVTVq1ND+/fvVq1evIh/H2XiudZopLi5Oo0eP1sWLF+Xn5ydJWrlypapXr55v+sndSGYAAKVa7dq1tWnTJh08eFBBQUGF/iAHBwdrxIgRGj58uHJzc3XrrbcqMzNT69evV1BQkHr37q169eppwYIFWrFihaKjo/Xuu+9q8+bNDutBateurRUrVmj37t0KCwvLNyXkiqSkJA0ZMkQhISHq2rWrsrKytGXLFp06dUpPPfWU0+0/e/asVq9erebNmyswMLDA52td6zTTQw89pAkTJigxMVGjR4/WTz/9pMmTJ2vcuHEen2biaiYAQKk2YsQI+fj4qFGjRgoPD1daWlqhdSdNmqRx48YpOTlZDRs2VOfOnfX555/bk5WBAwfq/vvvV48ePdSqVSudOHHCYZRGkvr3768GDRooNjZW4eHh+vbbb4sce79+/fTWW28pJSVFTZs2VXx8vFJSUlxaTNumTRsNHDhQPXr0UHh4uKZMmVLkeK4kNDRUq1at0n//+1/FxsZq0KBBeuqpp5xOuq6FzXj64u8SlpmZqdDQUGVkZCgkJKSkwwFwFVzN5L0uXLigAwcOKDo6WuXKlSvpcFAKXOlvypXfb0ZmAACApZHMAAAASyOZAQAAlkYyAwAALI1kBgAAWBr3mQFgDWuSCy5PGFW8cQDwOozMAAAASyOZAQAAlkYyAwAALI1kBgAAWBoLgAEA16awxdme4uKi73bt2qlFixaaPn26Z+K5TGJiok6fPq2lS5cWy/muJiUlRcOGDdPp06c9ep4LFy5o4MCB2rp1q3bt2qW77rqr2L4DRmYAANc9Y4yys7NLOgxLy8nJUUBAgIYMGaIOHToU67lJZgAApVZiYqLWrl2rV199VTabTTabTQcPHlRqaqpsNptWrFih2NhY+fv7a926dTLGaMqUKapTp44CAgLUvHlzffTRR/bj5eTkqG/fvoqOjlZAQIAaNGigV1991b49KSlJ8+fP16effmo/X2pqqg4ePCibzaZFixbptttuU0BAgG6++Wbt2bNHmzdvVmxsrIKCgtSlSxcdP37coQ3z5s1Tw4YNVa5cOcXExGjWrFn2bXnHXbJkiRISEhQYGKjmzZtrw4YNkqTU1FT16dNHGRkZ9niSkpI88l2XL19es2fPVv/+/VWtWjWPnKMwTDMBAEqtV199VXv27FGTJk00ceJESVJ4eLgOHjwoSXrmmWc0depU1alTRxUqVNBzzz2nJUuWaPbs2brhhhv09ddf6+GHH1Z4eLji4+OVm5urmjVratGiRapcubLWr1+vv/71r4qIiFD37t01YsQI7dq1S5mZmZo3b54kqVKlSjpy5Igkafz48Zo+fbpq1aqlxx57TD179lRISIheffVVBQYGqnv37ho3bpxmz54tSZo7d67Gjx+vGTNmqGXLltq2bZv69++v8uXLq3fv3vZ2jhkzRlOnTtUNN9ygMWPGqGfPntq7d6/atGmj6dOna9y4cdq9e7ckKSgoqMDvat26deratesVv8/Ro0dr9OjRRe8QDyGZAQCUWqGhofLz81NgYGCBowUTJ05Ux44dJUnnzp3TK6+8oq+++kpxcXGSpDp16uibb77Rm2++qfj4ePn6+mrChAn2/aOjo7V+/XotWrRI3bt3V1BQkAICApSVlVXg+UaMGKHOnTtLkoYOHaqePXtq9erVatu2rSSpb9++SklJsdefNGmSXn75Zd1///328+3cuVNvvvmmQzIzYsQIdevWTZI0YcIENW7cWHv37lVMTIxCQ0Nls9muOloSGxur7du3X7FOpUqVrri9pJDMAACuW7Gxsfb3O3fu1IULF+zJTZ6LFy+qZcuW9s9vvPGG3nrrLR06dEi//fabLl68qBYtWjh1vmbNmtnfV61aVZLUtGlTh7Jjx45Jko4fP67Dhw+rb9++6t+/v71Odna2QkNDCz1uRESEJOnYsWOKiYlxKi5JCggIUL169Zyu701IZgAA163y5cvb3+fm5kqSli1bpho1ajjU8/f3lyQtWrRIw4cP18svv6y4uDgFBwfrpZde0qZNm5w6n6+vr/29zWYrsCwvjrz/nTt3rlq1auVwHB8fn6seN29/ZzHNBACAl/Lz81NOTs5V6zVq1Ej+/v5KS0tTfHx8gXXWrVunNm3aaNCgQfayffv2Fel8V1O1alXVqFFD+/fvV69evYp8HGfjYZqpiL7++mu99NJL2rp1q44ePapPPvlE9913n327MUYTJkzQnDlzdOrUKbVq1UozZ85U48aNSy5oAO5R6L1J/lysYaD0q127tjZt2qSDBw8qKCio0B/k4OBgjRgxQsOHD1dubq5uvfVWZWZmav369QoKClLv3r1Vr149LViwQCtWrFB0dLTeffddbd68WdHR0Q7nW7FihXbv3q2wsLB8U0KuSEpK0pAhQxQSEqKuXbsqKytLW7Zs0alTp/TUU0853f6zZ89q9erVat68uQIDAxUYGJivnjummXbu3KmLFy/q5MmTOnPmjD05cnYarqhK9NLsc+fOqXnz5poxY0aB26dMmaJXXnlFM2bM0ObNm1WtWjV17NhRZ86cKeZIAZS0DftPFPgCrmbEiBHy8fFRo0aNFB4errS0tELrTpo0SePGjVNycrIaNmyozp076/PPP7cnKwMHDtT999+vHj16qFWrVjpx4oTDKI0k9e/fXw0aNFBsbKzCw8P17bffFjn2fv366a233lJKSoqaNm2q+Ph4paSkOCRPV9OmTRsNHDhQPXr0UHh4uKZMmVLkeK7mzjvvVMuWLfX5558rNTVVLVu2dFhv5Ck2Y4zx+FmcYLPZHEZmjDGqXr26hg0bpmeffVaSlJWVpapVq+rFF1/UgAEDnDpuZmamQkNDlZGRoZCQEE+FD8BVhYzMTMsueGSmddqcAsvj+k51W0i4sgsXLujAgQOKjo5WuXLlSjoclAJX+pty5ffba2+ad+DAAaWnp6tTp072Mn9/f8XHx2v9+vWF7peVlaXMzEyHFwAAKL28NplJT0+X9L9L1/JUrVrVvq0gycnJCg0Ntb8iIyM9GicAAChZXpvM5Mm7xCyPMSZf2R+NGjVKGRkZ9tfhw4c9HSIAAChBXntpdt6dCtPT0+03AJJ+vwnQ5aM1f+Tv72+/HwAAACj9vHZkJjo6WtWqVdOqVavsZRcvXtTatWvVpk2bEowMAK5vXnLdCEoBd/0tlejIzNmzZ7V371775wMHDmj79u2qVKmSatWqpWHDhmny5Mm64YYbdMMNN2jy5MkKDAzUQw89VIJRA8D1Ke8us+fPn1dAQEAJR4PS4Pz585Ic72BcFCWazGzZskUJCQn2z3k3AOrdu7dSUlL0zDPP6LffftOgQYPsN81buXKlgoODSypkAFZX2M36EkYVbxwW5OPjowoVKtifHRQYGHjFNYxAYYwxOn/+vI4dO6YKFSrkezyDq0o0mWnXrt0Vh5hsNpuSkpKUlJRUfEEBAAqVt54xL6EBrkWFChWu+jRvZ3jtAmAAgPex2WyKiIhQlSpVdOnSpZIOBxbm6+t7zSMyeUhmAAAu8/HxcdsPEXCtvPZqJgAAAGeQzAAAAEsjmQEAAJZGMgMAACyNZAYAAFgayQwAALA0khkAAGBpJDMAAMDSSGYAAIClkcwAAABLI5kBAACWRjIDAAAsjWQGAABYGskMAACwNJIZAABgaWVLOgAA+KPWaXNKOgQAFsPIDAAAsDSSGQAAYGkkMwAAwNJIZgAAgKWRzAAAAEvjaiYA7rEmueDyhFHFGweA6w4jMwAAwNJIZgAAgKWRzAAAAEtjzQwAt9iw/0SB5XEJxRzIVVglTgDOI5kBkM+0VXsKLB/esX4xRwIAV8c0EwAAsDSSGQAAYGkkMwAAwNJIZgAAgKWRzAAAAEsjmQEAAJZGMgMAACyNZAYAAFgaN80DUDoV9hRvAKUOIzMAAMDSSGYAAIClMc0EoFQq7IGSAEofkhkAJYJkA4C7kMwAsDYW+gLXPdbMAAAASyOZAQAAlkYyAwAALI1kBgAAWBoLgAE4bdqqPYVua12McQDAHzEyAwAALI1kBgAAWBrJDAAAsDSSGQAAYGkkMwAAwNK8OpnJzs7Wc889p+joaAUEBKhOnTqaOHGicnNzSzo0AADgJbz60uwXX3xRb7zxhubPn6/GjRtry5Yt6tOnj0JDQzV06NCSDg8AAHgBr05mNmzYoHvvvVfdunWTJNWuXVsLFy7Uli1bSjgyAADgLbx6munWW2/V6tWrtWfP7zfq+n//7//pm2++0Z133lnCkQEAAG/h1SMzzz77rDIyMhQTEyMfHx/l5OTohRdeUM+ePQvdJysrS1lZWfbPmZmZxREqUKq0TptTYPnGWn8t5kgA4Oq8emTmww8/1D/+8Q+9//77+v777zV//nxNnTpV8+fPL3Sf5ORkhYaG2l+RkZHFGDEAAChuXp3M/O1vf9PIkSP14IMPqmnTpnrkkUc0fPhwJScnF7rPqFGjlJGRYX8dPny4GCMGAADFzaunmc6fP68yZRzzLR8fnytemu3v7y9/f39PhwYAALyEVyczd999t1544QXVqlVLjRs31rZt2/TKK6/oscceK+nQAACAl/DqZOb111/X2LFjNWjQIB07dkzVq1fXgAEDNG7cuJIODQAAeAmvTmaCg4M1ffp0TZ8+vaRDAQAAXsqrFwADAABcDckMAACwNJIZAABgaV69ZgYALGlNIffCShhVvHEA1wmXR2YSExP19ddfeyIWAAAAl7k8MnPmzBl16tRJkZGR6tOnj3r37q0aNWp4IjYAXqawZzYBQElyeWTm448/1s8//6wnnnhCixcvVu3atdW1a1d99NFHunTpkidiBAAAKFSRFgCHhYVp6NCh2rZtm7777jvVq1dPjzzyiKpXr67hw4frp59+cnecAAAABbqmq5mOHj2qlStXauXKlfLx8dGdd96pH3/8UY0aNdK0adPcFSMAAEChXE5mLl26pI8//lh33XWXoqKitHjxYg0fPlxHjx7V/PnztXLlSr377ruaOHGiJ+IFAABw4PIC4IiICOXm5qpnz5767rvv1KJFi3x1OnfurAoVKrghPAAAgCtzOZmZNm2aHnjgAZUrV67QOhUrVtSBAweuKTAApcO0VXsKLG9dzHFcFfeGASzL5WmmNWvWFHjV0rlz5/TYY4+5JSgAAABnuZzMzJ8/X7/99lu+8t9++00LFixwS1AAAADOcnqaKTMzU8YYGWN05swZh2mmnJwcLV++XFWqVPFIkAAAAIVxOpmpUKGCbDabbDab6tevn2+7zWbThAkT3BocAPcobN3K8I75/78MAFbjdDKzZs0aGWN0xx136OOPP1alSpXs2/z8/BQVFaXq1at7JEgA8LQN+08UWB6XUMyBAHCZ08lMfHy8JOnAgQOqVauWbDabx4ICAABwllPJzI4dO9SkSROVKVNGGRkZ+uGHHwqt26xZM7cFBwAAcDVOJTMtWrRQenq6qlSpohYtWshms8kYk6+ezWZTTk6O24MEAAAojFPJzIEDBxQeHm5/DwAA4C2cSmaioqIKfA8AJa2whbsArh9FumnesmXL7J+feeYZVahQQW3atNGhQ4fcGhwAAMDVuJzMTJ48WQEBAZKkDRs2aMaMGZoyZYoqV66s4cOHuz1AAACAK3H5QZOHDx9WvXr1JElLly7VX/7yF/31r39V27Zt1a5dO3fHBwAAcEUuj8wEBQXpxInf56hXrlypDh06SJLKlStX4DObAAAAPMnlkZmOHTuqX79+atmypfbs2aNu3bpJkn788UfVrl3b3fEBAABckcsjMzNnzlRcXJyOHz+ujz/+WGFhYZKkrVu3qmfPnm4PEAAA4EpcHpmpUKGCZsyYka+ch0wCAICS4HIyI0mnT5/Wd999p2PHjik3N9debrPZ9Mgjj7gtOAAAgKtxOZn5/PPP1atXL507d07BwcEOD5wkmQEAAMXN5TUzTz/9tB577DGdOXNGp0+f1qlTp+yvkydPeiJGAACAQrmczPz8888aMmSIAgMDPREPAACAS1xOZjp37qwtW7Z4IhYAAACXubxmplu3bvrb3/6mnTt3qmnTpvL19XXYfs8997gtOAAAgKtxOZnp37+/JGnixIn5ttlsNuXk5Fx7VAAAAE5yOZn546XYAHA1rdPmlHQIAEo5l9fM/NGFCxfcFQcAAECRuJzM5OTkaNKkSapRo4aCgoK0f/9+SdLYsWP19ttvuz1AAACAK3F5mumFF17Q/PnzNWXKFPv6GUlq2rSppk2bpr59+7o1QADXrvCpnqnFGgcAeILLIzMLFizQnDlz1KtXL/n4+NjLmzVrpv/85z9uDQ4AAOBqinTTvHr16uUrz83N1aVLl9wSFAAAgLNcTmYaN26sdevW5StfvHixWrZs6ZagAAAAnOXympnx48frkUce0c8//6zc3FwtWbJEu3fv1oIFC/TFF194IkYAAIBCuTwyc/fdd+vDDz/U8uXLZbPZNG7cOO3atUuff/65Onbs6IkYAQAACuXyyIz0+/OZOnfu7O5YAAAAXFakZAYArhtrkgsuTxhVvHEAKJRTyUzFihVls9mcOuDJkyevKSAAAABXOJXMTJ8+3f7+xIkTev7559W5c2fFxcVJkjZs2KAVK1Zo7NixHgkSAACgME4lM71797a///Of/6yJEyfqiSeesJcNGTJEM2bM0L/+9S8NHz7c/VECAAAUwuWrmVasWKEuXbrkK+/cubP+9a9/uSUoAAAAZ7mczISFhemTTz7JV7506VKFhYW5JSgAAABnuXw104QJE9S3b1+lpqba18xs3LhRX375pd566y23BwgApR5XTAHXxOVkJjExUQ0bNtRrr72mJUuWyBijRo0a6dtvv1WrVq08ESMAAEChinSfmVatWum9995zdywF+vnnn/Xss8/qn//8p3777TfVr19fb7/9tm666aZiOT8AAPBuXn3TvFOnTqlt27ZKSEjQP//5T1WpUkX79u1ThQoVSjo0AADgJbw6mXnxxRcVGRmpefPm2ctq165dcgEBXm7aqj0Flrcu5jgAoDi5fDVTcfrss88UGxurBx54QFWqVFHLli01d+7cK+6TlZWlzMxMhxcAACi9vDqZ2b9/v2bPnq0bbrhBK1as0MCBAzVkyBAtWLCg0H2Sk5MVGhpqf0VGRhZjxAAAoLi5nMykpKTo/Pnznogln9zcXN14442aPHmyWrZsqQEDBqh///6aPXt2ofuMGjVKGRkZ9tfhw4eLJVYAAFAyXE5mRo0apWrVqqlv375av369J2Kyi4iIUKNGjRzKGjZsqLS0tEL38ff3V0hIiMMLAACUXi4nM//973/1j3/8Q6dOnVJCQoJiYmL04osvKj093e3BtW3bVrt373Yo27Nnj6Kiotx+LgAAYE0uJzM+Pj665557tGTJEh0+fFh//etf9d5776lWrVq655579Omnnyo3N9ctwQ0fPlwbN27U5MmTtXfvXr3//vuaM2eOBg8e7JbjA8DVbNh/osAXAO9xTQuAq1SporZt2youLk5lypTRDz/8oMTERNWtW1epqanXHNzNN9+sTz75RAsXLlSTJk00adIkTZ8+Xb169brmYwMAgNKhSMnML7/8oqlTp6px48Zq166dMjMz9cUXX+jAgQM6cuSI7r//fvXu3dstAd5111364YcfdOHCBe3atUv9+/d3y3EBAEDp4PJN8+6++26tWLFC9evXV//+/fXoo4+qUqVK9u0BAQF6+umnNW3aNLcGCuB/Crs5HgBcj1xOZqpUqaK1a9fan5hdkIiICB04cOCaAgMAqypsTU2cCnk6NoBr4vI0U3x8vG688cZ85RcvXrTfzM5ms3HFEQAAKBYuj8z06dNHXbp0UZUqVRzKz5w5oz59+ujRRx91W3AAcF1bU8hITsKo4o0D8HIuj8wYY2Sz2fKV//e//1VoaKhbggIAAHCW0yMzLVu2lM1mk81mU/v27VW27P92zcnJ0YEDB9SlSxePBAkAAFAYp5OZ++67T5K0fft2de7cWUFBQfZtfn5+ql27tv785z+7PUAAAIArcTqZGT9+vCSpdu3a6tGjh8qVK+exoAAUj8Iu8W5dzHEAwLVweQGwu26GBwAA4A5OJTOVKlXSnj17VLlyZVWsWLHABcB5Tp486bbgAAAArsapZGbatGkKDg62v79SMgPAOlqnzSnpEADgmjmVzPxxaikxMdFTsQAAALjMqWQmMzPT6QOGhIQUORgAAABXOZXMVKhQ4apTS3k308vJyXFLYAAAAM5wKplZs2aNp+MAAAAoEqeSmfj4eE/HAQAAUCROJTM7duxQkyZNVKZMGe3YseOKdZs1a+aWwAAAAJzhVDLTokULpaenq0qVKmrRooVsNpuMMfnqsWYGwHWjsCdaAyh2TiUzBw4cUHh4uP09AACAt3AqmYmKiirwPQAAQElz+dlMkrR79269/vrr2rVrl2w2m2JiYvTkk0+qQYMG7o4PALzShv0nSjoEAP+njKs7fPTRR2rSpIm2bt2q5s2bq1mzZvr+++/VpEkTLV682BMxAgAAFMrlkZlnnnlGo0aN0sSJEx3Kx48fr2effVYPPPCA24IDgNKksNGcuDphxRwJULq4PDKTnp6uRx99NF/5ww8/rPT0dLcEBQAA4CyXk5l27dpp3bp1+cq/+eYb3XbbbW4JCgAAwFlOTTN99tln9vf33HOPnn32WW3dulWtW7eWJG3cuFGLFy/WhAkTPBMlAABAIWymoLvfXaZMGecGcLzxpnmZmZkKDQ1VRkYGT/RGqTFt1R6X6rdOm+OhSOAOLq+ZSRjlmUAAL+LK77dTIzO5ubluCQwAAMDdXF4zAwAA4E2KdNO8c+fOae3atUpLS9PFixcdtg0ZMsQtgQEAADjD5WRm27ZtuvPOO3X+/HmdO3dOlSpV0q+//qrAwEBVqVKFZAYA3KTQ+9IkFHMggJdzeZpp+PDhuvvuu3Xy5EkFBARo48aNOnTokG666SZNnTrVEzECAAAUyuVkZvv27Xr66afl4+MjHx8fZWVlKTIyUlOmTNHo0aM9ESMAAEChXE5mfH19ZbPZJElVq1ZVWlqaJCk0NNT+HgAAoLi4vGamZcuW2rJli+rXr6+EhASNGzdOv/76q9599101bdrUEzECAAAUyuWRmcmTJysiIkKSNGnSJIWFhenxxx/XsWPHNGcON+YCAADFy+WRmdjYWPv78PBwLV++3K0BAQAAuKJI95mRpGPHjmn37t2y2Wxq0KCBwsPD3RkXAACAU1yeZsrMzNQjjzyiGjVqKD4+XrfffruqV6+uhx9+WBkZGZ6IEQAAoFAuJzP9+vXTpk2b9MUXX+j06dPKyMjQF198oS1btqh///6eiBEAAKBQLk8zLVu2TCtWrNCtt95qL+vcubPmzp2rLl26uDU4AK7h6dgArkcuj8yEhYUpNDQ0X3loaKgqVqzolqAAAACc5XIy89xzz+mpp57S0aNH7WXp6en629/+prFjx7o1OAAAgKtxapqpZcuW9rv+StJPP/2kqKgo1apVS5KUlpYmf39/HT9+XAMGDPBMpAAAAAVwKpm57777PBwGAABA0TiVzIwfP97TcQAAABRJkW+at3XrVu3atUs2m02NGjVSy5Yt3RkXcF2ZtmpPgeXDO9Yv5kgAwHpcTmaOHTumBx98UKmpqapQoYKMMcrIyFBCQoI++OAD7gQMAACKlctXMz355JPKzMzUjz/+qJMnT+rUqVP697//rczMTA0ZMsQTMQIAABTK5ZGZL7/8Uv/617/UsGFDe1mjRo00c+ZMderUya3BAQAAXI3LIzO5ubny9fXNV+7r66vc3Fy3BAUAAOAsl5OZO+64Q0OHDtWRI0fsZT///LOGDx+u9u3buzU4AACAq3E5mZkxY4bOnDmj2rVrq27duqpXr56io6N15swZvf76656IEQAAoFAuJzORkZH6/vvvtWzZMg0bNkxDhgzR8uXLtXXrVtWsWdMTMdolJyfLZrNp2LBhHj0PAACwDpcWAGdnZ6tcuXLavn27OnbsqI4dO3oqrnw2b96sOXPmqFmzZsV2TgAA4P1cGpkpW7asoqKilJOT46l4CnT27Fn16tVLc+fO5cncAADAQZGemj1q1CidPHnSE/EUaPDgwerWrZs6dOhQbOcEAADW4PJ9Zl577TXt3btX1atXV1RUlMqXL++w/fvvv3dbcJL0wQcf6Pvvv9fmzZudqp+VlaWsrCz758zMTLfGAwDutmH/iZIOAbA0l5OZe++9VzabzROx5HP48GENHTpUK1euVLly5ZzaJzk5WRMmTPBwZAAAwFvYjDGmpIMozNKlS/WnP/1JPj4+9rKcnBzZbDaVKVNGWVlZDtukgkdmIiMjlZGRoZCQkGKLHXCFqw+aLKx+67Q5bosJ3iuu79SSDgHwuMzMTIWGhjr1++30yMz58+f1t7/9TUuXLtWlS5fUoUMHvfbaa6pcufI1B1yY9u3b64cffnAo69Onj2JiYvTss8/mS2Qkyd/fX/7+/h6LCQBK3JrkgssTRhVvHICXcDqZGT9+vFJSUtSrVy+VK1dOCxcu1OOPP67Fixd7LLjg4GA1adLEoax8+fIKCwvLVw4AAK5PTiczS5Ys0dtvv60HH3xQkvTwww+rbdu2ysnJKXCEBAAAoDg4ncwcPnxYt912m/3zLbfcorJly+rIkSOKjIz0SHAFSU1NLbZzAd6KtTEA8D9O32cmJydHfn5+DmVly5ZVdna224MCAABwltMjM8YYJSYmOiyuvXDhggYOHOhwr5klS5a4N0IAAIArcDqZ6d27d76yhx9+2K3BAAAAuMrpZGbevHmejAMAAKBIXH42EwAAgDchmQEAAJZGMgMAACyNZAYAAFgayQwAALA0khkAAGBpTl+aDQDwcjxNG9cpRmYAAIClkcwAAABLI5kBAACWRjIDAAAsjWQGAABYGskMAACwNJIZAABgaSQzAADA0khmAACApZHMAAAASyOZAQAAlkYyAwAALI1kBgAAWBpPzQa82LRVewosb13McQCAN2NkBgAAWBrJDAAAsDSmmQDAYjbsP1FgeVydsGKOBPAOJDNAMSpsDQwAoOiYZgIAAJZGMgMAACyNZAYAAFgaa2YAoLRbk1xwecKo4o0D8BBGZgAAgKWRzAAAAEtjmgnwAq3T5hRYvrHWX4s5EgCwHkZmAACApZHMAAAASyOZAQAAlsaaGcADeGwBABQfRmYAAIClkcwAAABLY5oJAEqJDftPFFgeVyesmCMBihcjMwAAwNJIZgAAgKWRzAAAAEsjmQEAAJZGMgMAACyNq5kAoJQr9CqnhGIOBPAQRmYAAIClkcwAAABLI5kBAACWRjIDAAAsjWQGAABYmlcnM8nJybr55psVHBysKlWq6L777tPu3btLOiwAAOBFvDqZWbt2rQYPHqyNGzdq1apVys7OVqdOnXTu3LmSDg0AAHgJr77PzJdffunwed68eapSpYq2bt2q22+/vYSiAgAA3sSrk5nLZWRkSJIqVapUaJ2srCxlZWXZP2dmZno8LsBTWqfNKekQAMDrWSaZMcboqaee0q233qomTZoUWi85OVkTJkwoxsgAwKLWJBdcnjCqeOMArpFXr5n5oyeeeEI7duzQwoULr1hv1KhRysjIsL8OHz5cTBECAICSYImRmSeffFKfffaZvv76a9WsWfOKdf39/eXv719MkQEAgJLm1cmMMUZPPvmkPvnkE6Wmpio6OrqkQwIAAF7Gq5OZwYMH6/3339enn36q4OBgpaenS5JCQ0MVEBBQwtEBAABv4NVrZmbPnq2MjAy1a9dOERER9teHH35Y0qEBAAAv4dUjM8aYkg4BuKJpq/YUWF7YJdUba/3Vk+EAwHXJq0dmAAAArsarR2YAACWA+8/AYhiZAQAAlsbIDABcpzbsP1FgeVydsGKOBLg2jMwAAABLI5kBAACWRjIDAAAsjWQGAABYGskMAACwNK5mAq5BYXf6dVd9AMDVMTIDAAAsjWQGAABYGtNMwP8p7KGRwzvWL+ZIAACuYGQGAABYGskMAACwNKaZAADXjidtowQxMgMAACyNkRkAgOcwYoNiwMgMAACwNEZmAAAes2H/iQLL4xKKORCUaiQzAAAHJCCwGqaZAACApTEyg+tOYXf6LUr91tcaDADgmjEyAwAALI2RGQCAcwq7zBooYSQzAACnFLYwWJLi6oQVYySAI5IZAEDx42Z6cCOSGeD/tE6bU2D5xlp/LeZIAACuYAEwAACwNEZmAADX7ErraQBPI5lBqeXq/WQAANZEMgNcRWFraQAA3oE1MwAAwNJIZgAAgKUxzYTrDtNGAFC6MDIDAAAsjWQGAABYGskMAACwNNbMwPI2vD2i4A08hgAArguMzAAAAEtjZAZep7A79w7vWL+YIwEAWAEjMwAAwNIYmUGpxf1kAOD6QDIDACh91iQXXJ4wqnjjQLEgmYFlFLaWpnUxxwEA8C4kMwAA78GICoqABcAAAMDSSGYAAIClkcwAAABLY80MAKDYbdh/osDyuDphnj1+glsODy9DMgOP446+AABPIplBiSksyQEAwBUkM/C4wu7Eu9HFp1pzR18AQEEskczMmjVLL730ko4eParGjRtr+vTpuu2220o6LFyGm9oB8BRXR3Ld9e8O0+TW4PXJzIcffqhhw4Zp1qxZatu2rd5880117dpVO3fuVK1atUo6PHgAIzDA9auwhbut5WX/LhR2cz/J+27wdx3ciNDrk5lXXnlFffv2Vb9+/SRJ06dP14oVKzR79mwlJ1/hjwkO+K8LAEBp5dXJzMWLF7V161aNHDnSobxTp05av359CUXlJa70XwUF+rNLtYtjcS4jMACKXSH/dhY+IlSwDVc4RZxc/Pe5sBESV0dUXP1dKEUjNl6dzPz666/KyclR1apVHcqrVq2q9PT0AvfJyspSVlaW/XNGRoYkKTMz0yMxzvxqb4Hlg++o59njl73g0nEuZJ8tsLyw7+XCuYLrF8W537KuXgkAisG/fjzi8XNknnPt32cV9vtU2HFcre+qQo7v6d+7/GH8Hocx5qp1vTqZyWOz2Rw+G2PyleVJTk7WhAkT8pVHRkZ6JLbCjPa6409003EAAO5V8L/P7qvvKteO7+nfkTNnzig0NPSKdbw6malcubJ8fHzyjcIcO3Ys32hNnlGjRumpp56yf87NzdXJkycVFhZWaAJU0jIzMxUZGanDhw8rJCSkpMPxmOuhnbSxdKCNpQNttDZjjM6cOaPq1atfta5XJzN+fn666aabtGrVKv3pT3+yl69atUr33ntvgfv4+/vL39/foaxChQqeDNNtQkJCSt0fY0Guh3bSxtKBNpYOtNG6rjYik8erkxlJeuqpp/TII48oNjZWcXFxmjNnjtLS0jRw4MCSDg0AAHgBr09mevTooRMnTmjixIk6evSomjRpouXLlysqKqqkQwMAAF7A65MZSRo0aJAGDRpU0mF4jL+/v8aPH59veqy0uR7aSRtLB9pYOtDG64fNOHPNEwAAgJcqU9IBAAAAXAuSGQAAYGkkMwAAwNJIZgAAgKWRzHjIrFmzFB0drXLlyummm27SunXrCq179OhRPfTQQ2rQoIHKlCmjYcOG5auTkpIim82W73XhgpuexVEErrRxyZIl6tixo8LDwxUSEqK4uDitWLEiX72PP/5YjRo1kr+/vxo1aqRPPvnEk024Kne30er9+M0336ht27YKCwtTQECAYmJiNG3atHz1rNyPzrTR6v34R99++63Kli2rFi1a5Ntm5X78o8La6I39KLnWztTU1ALb8J///Mehnrf1pdsZuN0HH3xgfH19zdy5c83OnTvN0KFDTfny5c2hQ4cKrH/gwAEzZMgQM3/+fNOiRQszdOjQfHXmzZtnQkJCzNGjRx1eJcXVNg4dOtS8+OKL5rvvvjN79uwxo0aNMr6+vub777+311m/fr3x8fExkydPNrt27TKTJ082ZcuWNRs3biyuZjnwRBut3o/ff/+9ef/9982///1vc+DAAfPuu++awMBA8+abb9rrWL0fnWmj1fsxz+nTp02dOnVMp06dTPPmzR22Wb0f81ypjd7Wj8a43s41a9YYSWb37t0ObcjOzrbX8ba+9ASSGQ+45ZZbzMCBAx3KYmJizMiRI6+6b3x8fKHJTGhoqJsivHbX0sY8jRo1MhMmTLB/7t69u+nSpYtDnc6dO5sHH3zw2oItIk+0sTT245/+9Cfz8MMP2z+Xxn68vI2lpR979OhhnnvuOTN+/Ph8P/SlpR+v1EZv60djXG9nXjJz6tSpQo/pbX3pCUwzudnFixe1detWderUyaG8U6dOWr9+/TUd++zZs4qKilLNmjV11113adu2bdd0vKJyRxtzc3N15swZVapUyV62YcOGfMfs3LnzNX9vReGpNkqlqx+3bdum9evXKz4+3l5W2vqxoDZK1u/HefPmad++fRo/fnyB20tDP16tjZL39KN0bX+vLVu2VEREhNq3b681a9Y4bPOmvvQUkhk3+/XXX5WTk5Pvqd5Vq1bN9/RvV8TExCglJUWfffaZFi5cqHLlyqlt27b66aefrjVkl7mjjS+//LLOnTun7t2728vS09Pd/r0VlafaWFr6sWbNmvL391dsbKwGDx6sfv362beVln68Uhut3o8//fSTRo4cqffee09lyxZ8I3ir96MzbfSmfpSK1s6IiAjNmTNHH3/8sZYsWaIGDRqoffv2+vrrr+11vKkvPcUSjzOwIpvN5vDZGJOvzBWtW7dW69at7Z/btm2rG2+8Ua+//rpee+21Ih/3WhS1jQsXLlRSUpI+/fRTValSxS3H9BR3t7G09OO6det09uxZbdy4USNHjlS9evXUs2fPazqmJ7m7jVbux5ycHD300EOaMGGC6tev75ZjFhd3t9Eb+1Fy7Xtv0KCBGjRoYP8cFxenw4cPa+rUqbr99tuLdEwrIplxs8qVK8vHxydfxnvs2LF8mfG1KFOmjG6++eYS+S+Ia2njhx9+qL59+2rx4sXq0KGDw7Zq1ap5/HtzlqfaeDmr9mN0dLQkqWnTpvrll1+UlJRk/6EvLf14pTZezkr9eObMGW3ZskXbtm3TE088Ien3KVFjjMqWLauVK1fqjjvusHQ/OtvGy5VkP0ru+/1o3bq1/vGPf9g/e1NfegrTTG7m5+enm266SatWrXIoX7Vqldq0aeO28xhjtH37dkVERLjtmM4qahsXLlyoxMREvf/+++rWrVu+7XFxcfmOuXLlSrd+b87yVBsvZ8V+vJwxRllZWfbPpaEfL3d5GwvabpV+DAkJ0Q8//KDt27fbXwMHDlSDBg20fft2tWrVSpK1+9HZNl6uJPtRct/f67Zt2xza4E196THFu974+pB3ad3bb79tdu7caYYNG2bKly9vDh48aIwxZuTIkeaRRx5x2Gfbtm1m27Zt5qabbjIPPfSQ2bZtm/nxxx/t25OSksyXX35p9u3bZ7Zt22b69OljypYtazZt2lSsbcvjahvff/99U7ZsWTNz5kyHywdPnz5tr/Ptt98aHx8f8/e//93s2rXL/P3vf/eKS0Hd2Uar9+OMGTPMZ599Zvbs2WP27Nlj3nnnHRMSEmLGjBljr2P1fnSmjVbvx8sVdKWP1fvxcgW10dv60RjX2zlt2jTzySefmD179ph///vfZuTIkUaS+fjjj+11vK0vPYFkxkNmzpxpoqKijJ+fn7nxxhvN2rVr7dt69+5t4uPjHepLyveKioqybx82bJipVauW8fPzM+Hh4aZTp05m/fr1xdSagrnSxvj4+ALb2Lt3b4djLl682DRo0MD4+vqamJgYh/9DlgR3t9Hq/fjaa6+Zxo0bm8DAQBMSEmJatmxpZs2aZXJychyOaeV+dKaNVu/HyxX0Q2+MtfvxcgW10Rv70RjX2vniiy+aunXrmnLlypmKFSuaW2+91SxbtizfMb2tL93NZowxxT8eBAAA4B6smQEAAJZGMgMAACyNZAYAAFgayQwAALA0khkAAGBpJDMAAMDSSGYAAIClkcwAwP9JTU2VzWbT6dOnSzoUAC4gmQHgssTERNlsNtlsNvn6+qpOnToaMWKEzp07V9KhXZM2bdro6NGjCg0NlSSlpKSoQoUKJRsUgKviqdkAiqRLly6aN2+eLl26pHXr1qlfv346d+6cZs+e7VDv0qVL8vX1LaEoXePn56dq1aqVdBgAXMTIDIAi8ff3V7Vq1RQZGamHHnpIvXr10tKlS5WUlKQWLVronXfeUZ06deTv7y9jjNLS0nTvvfcqKChIISEh6t69u3755Rf78fL2e/PNNxUZGanAwEA98MAD+aZ85s2bp4YNG6pcuXKKiYnRrFmz7NsOHjwom82mJUuWKCEhQYGBgWrevLk2bNhgr3Po0CHdfffdqlixosqXL6/GjRtr+fLlkhynmVJTU9WnTx9lZGTYR6GSkpI0ceJENW3aNN/3cdNNN2ncuHFu/pYBOINkBoBbBAQE6NKlS5KkvXv3atGiRfr444+1fft2SdJ9992nkydPau3atVq1apX27dunHj16OBwjb7/PP/9cX375pbZv367Bgwfbt8+dO1djxozRCy+8oF27dmny5MkaO3as5s+f73CcMWPGaMSIEdq+fbvq16+vnj17Kjs7W5I0ePBgZWVl6euvv9YPP/ygF198UUFBQfna06ZNG02fPl0hISE6evSojh49qhEjRuixxx7Tzp07tXnzZnvdHTt2aNu2bUpMTHTHVwnAVSX8oEsAFtS7d29z77332j9v2rTJhIWFme7du5vx48cbX19fc+zYMfv2lStXGh8fH5OWlmYv+/HHH40k89133xljfn+qsY+Pjzl8+LC9zj//+U9TpkwZc/ToUWOMMZGRkeb99993iGXSpEkmLi7OGGPMgQMHjCTz1ltv5TvPrl27jDHGNG3a1CQlJRXYrjVr1hhJ5tSpU8YYY+bNm2dCQ0Pz1evatat5/PHH7Z+HDRtm2rVrV+j3BcCzGJkBUCRffPGFgoKCVK5cOcXFxen222/X66+/LkmKiopSeHi4ve6uXbsUGRmpyMhIe1mjRo1UoUIF7dq1y15Wq1Yt1axZ0/45Li5Oubm52r17t44fP67Dhw+rb9++CgoKsr+ef/557du3zyG2Zs2a2d9HRERIko4dOyZJGjJkiJ5//nm1bdtW48eP144dO1xue//+/bVw4UJduHBBly5d0nvvvafHHnvM5eMAcA8WAAMokoSEBM2ePVu+vr6qXr26wyLf8uXLO9Q1xshms+U7RmHlefK22Ww25ebmSvp9qqlVq1YO9Xx8fBw+/zGWvGPk7d+vXz917txZy5Yt08qVK5WcnKyXX35ZTz755FXbnOfuu++Wv7+/PvnkE/n7+ysrK0t//vOfnd4fgHuRzAAokvLly6tevXpO1W3UqJHS0tJ0+PBh++jMzp07lZGRoYYNG9rrpaWl6ciRI6pevbokacOGDSpTpozq16+vqlWrqkaNGtq/f7969ep1TbFHRkZq4MCBGjhwoEaNGqW5c+cWmMz4+fkpJycnX3nZsmXVu3dvzZs3T/7+/nrwwQcVGBh4TTEBKDqSGQAe16FDBzVr1ky9evXS9OnTlZ2drUGDBik+Pl6xsbH2euXKlVPv3r01depUZWZmasiQIerevbv9cumkpCQNGTJEISEh6tq1q7KysrRlyxadOnVKTz31lFOxDBs2TF27dlX9+vV16tQpffXVVw4J1R/Vrl1bZ8+e1erVq9W8eXMFBgbak5Z+/frZ9/v222+v5esBcI1YMwPA42w2m5YuXaqKFSvq9ttvV4cOHVSnTh19+OGHDvXq1aun+++/X3feeac6deqkJk2aOFx63a9fP7311ltKSUlR06ZNFR8fr5SUFEVHRzsdS05OjgYPHqyGDRuqS5cuatCggcM5/qhNmzYaOHCgevToofDwcE2ZMsW+7YYbblCbNm3UoEGDfNNeAIqXzRhjSjoIAEhKStLSpUvtl3J7O2OMYmJiNGDAAKdHhQB4BtNMAOCiY8eO6d1339XPP/+sPn36lHQ4wHWPZAYAXFS1alVVrlxZc+bMUcWKFUs6HOC6xzQTAACwNBYAAwAASyOZAQAAlkYyAwAALI1kBgAAWBrJDAAAsDSSGQAAYGkkMwAAwNJIZgAAgKWRzAAAAEv7/womhwk8+4jhAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_propensity_score_distribution(\n",
+    "    propensity=data_ps['propensity_score'],\n",
+    "    treatment=data_ps['intervention'],\n",
+    "    reflect=False,\n",
+    "    kde=False,\n",
+    "    norm_hist=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "하지만 이는 하나의 데이터에 적합 및 예측을 동시에 하기 때문에 Overlap이 실제보다 좋아보일 수 있습니다."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "그렇다면 OOF 폴드 예측으로 얻은 PS 분포를 그려봅시다.   \n",
+    "(cv = None -> 단일 적합, cv = \"auto\" -> 교차검증)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Propensity Distribution'}, xlabel='Propensity', ylabel='Probability density'>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "res = evaluate(weight_model, X, T, Y, cv=\"auto\")\n",
+    "res.plot_weight_distribution(phase=\"valid\", reflect=False, norm_hist=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "OOF로 검증해도 Propensity Score의 Overlap 및 Positivity가 양호한 것을 확인"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ROC Curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'ROC Curve'}, xlabel='False Positive Rate', ylabel='True Positive Rate'>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "res.plot_roc_curve(phase=\"valid\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "위의 그래프는 우리가 구한 Propensity Score의 Overlap 정도를 확인하기 위한 ROC Curve이다(AUC가 0.5에 가까울수록 as-if random).    \n",
+    "- Propensity: 가중치 없이 Propensity Score 자체만으로 보았을 때\n",
+    "- Weighted: IPW 가중을 걸었을 때 -> 만약 Propensity Score와의 차이가 크면 극단 PS에 의해 분산 및 불안정성이 커진 신호\n",
+    "- Expected: 무작위 배정 레퍼런스 -> 위 두 경우의 AUC가 Expected와 크게 다르면 PS가 과도하게 극단이거나 표본 불균형 존재\n",
+    "\n",
+    "그래프 해석: Facure의 책에서 Treatment가 완전 무작위가 아니라고 밝혔다. 하지만, 우리가 잡은 \"X 기준으로 보았을 때(Given X)\"는 ROC Curve로 본 Treatment는 거의 랜덤에 준한다고 판단할 수 있다."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Double Machine Learning (비모수 버전의 Regression 처럼 활용 가능)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python (bayes311)",
    "language": "python",
-   "name": "python3"
+   "name": "bayes311"
   },
   "language_info": {
    "codemirror_mode": {
@@ -47,7 +2590,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.11.13"
   }
  },
  "nbformat": 4,

From bb36ac9f2a6e18941445a07b51a333eb7a59593d Mon Sep 17 00:00:00 2001
From: MinSeok Kang <rkdals1148@gmail.com>
Date: Fri, 3 Oct 2025 01:06:14 +0800
Subject: [PATCH 3/6] feat: update propensity_score_and_dml notebook (recreated
 locally)

---
 book/ate/propensity_score_and_dml.ipynb | 352 +++++++++++++++++++-----
 1 file changed, 276 insertions(+), 76 deletions(-)

diff --git a/book/ate/propensity_score_and_dml.ipynb b/book/ate/propensity_score_and_dml.ipynb
index 07f6103..c93a5bf 100644
--- a/book/ate/propensity_score_and_dml.ipynb
+++ b/book/ate/propensity_score_and_dml.ipynb
@@ -2291,6 +2291,20 @@
     "각각의 수치에 대한 검증은 필수입니다.  \n",
     "어떤 수치가 더 Robust하게 추정이 된 걸까요?   \n",
     "\n",
+    "Yang et al., 2019, Gastrointest Endosc 및 Austin, 2021, Statistic in Medicine 논문을 참고하여 IPW에 대한 Robustness Check을 진행해보도록 하겠습니다.    \n",
+    "순서는 다음과 같습니다.\n",
+    "\n",
+    "1) **IPW 가중 전, 후 |SMD| 변화 확인**\n",
+    "\n",
+    "2) **Propensity Score의 Overlap 확인**\n",
+    "\n",
+    "3) **IPW 가중 이후 ESS 및 VIF 확인**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "먼저 IPW로 구한 ATE의 신빙성을 테스트 해보겠습니다."
    ]
   },
@@ -2316,7 +2330,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### IPW 가중 후 |SMD| 변화 확인"
+    "### IPW 가중 전, 후 |SMD| 변화 확인"
    ]
   },
   {
@@ -2353,53 +2367,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Weighted이후에 |SMD|가 더 줄어든 것을 보았을 때, Weighted 이후 밸런스가 개선되었음을 확인할 수 있습니다."
+    "Weighted이후에 |SMD|가 더 줄어든 것을 보았을 때, Weighted 이후 밸런스가 개선되었음을 확인할 수 있습니다.    \n",
+    "|SMD| < 0.1 이 목표"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### IPW 가중치 분포 및 유효표본수(ESS)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ESS: 10354.681665498   (N: 10391 )\n"
-     ]
-    }
-   ],
-   "source": [
-    "w = weight_model.compute_weights(X, T)\n",
-    "ESS = (w.sum()**2) / (w**2).sum()\n",
-    "print(\"ESS:\", float(ESS), \"  (N:\", len(w), \")\")"
+    "### Propensity Score Overlap\n",
+    "아래는 처음에 Logistic Regression으로 추정한 Propensity Score의 Distribution을 나타낸 것입니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5, 1.0, 'IPW weights (log y)')"
+       "<Axes: title={'center': 'Propensity Distribution'}, xlabel='Propensity', ylabel='Probability density'>"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2409,28 +2406,33 @@
     }
    ],
    "source": [
-    "plt.figure(); plt.hist(w, bins=40); plt.yscale('log'); plt.title('IPW weights (log y)')"
+    "plot_propensity_score_distribution(\n",
+    "    propensity=data_ps['propensity_score'],\n",
+    "    treatment=data_ps['intervention'],\n",
+    "    reflect=False,\n",
+    "    kde=False,\n",
+    "    norm_hist=True,\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "1) ESS와 N수가 거의 비슷한 것을 확인할 수 있음 -> 가중치가 고르게 퍼져있다\n",
-    "2) 히스토그램이 1 주변에 모여있고(= 가중치 과도하게 쏠리지 않음), 꼬리 부분도 완만(= 극단 가중치 거의 없음)"
+    "하지만 이는 하나의 데이터에 적합 및 예측을 동시에 하기 때문에 Overlap이 실제보다 좋아보일 수 있습니다."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Propensity Score Overlap\n",
-    "아래는 처음에 Logistic Regression으로 추정한 Propensity Score의 Distribution을 나타낸 것입니다."
+    "그렇다면 OOF 폴드 예측으로 얻은 PS 분포를 그려봅시다.   \n",
+    "(cv = None -> 단일 적합, cv = \"auto\" -> 교차검증)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -2439,13 +2441,13 @@
        "<Axes: title={'center': 'Propensity Distribution'}, xlabel='Propensity', ylabel='Probability density'>"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2455,48 +2457,64 @@
     }
    ],
    "source": [
-    "plot_propensity_score_distribution(\n",
-    "    propensity=data_ps['propensity_score'],\n",
-    "    treatment=data_ps['intervention'],\n",
-    "    reflect=False,\n",
-    "    kde=False,\n",
-    "    norm_hist=True,\n",
-    ")"
+    "res = evaluate(weight_model, X, T, Y, cv=\"auto\")\n",
+    "res.plot_weight_distribution(phase=\"valid\", reflect=False, norm_hist=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "하지만 이는 하나의 데이터에 적합 및 예측을 동시에 하기 때문에 Overlap이 실제보다 좋아보일 수 있습니다."
+    "OOF로 검증해도 Propensity Score의 Overlap 및 Positivity가 양호한 것을 확인"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "그렇다면 OOF 폴드 예측으로 얻은 PS 분포를 그려봅시다.   \n",
-    "(cv = None -> 단일 적합, cv = \"auto\" -> 교차검증)"
+    "### IPW 가중치 분포 및 유효표본수(ESS)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ESS = 10354.68 (N = 10391)  ->  VIF = 1.0035\n"
+     ]
+    }
+   ],
+   "source": [
+    "w = weight_model.compute_weights(X, T)\n",
+    "ESS = (w.sum()**2) / (w**2).sum()\n",
+    "N   = len(w)\n",
+    "VIF = N / ESS\n",
+    "\n",
+    "print(f\"ESS = {ESS:.2f} (N = {N})  ->  VIF = {VIF:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Axes: title={'center': 'Propensity Distribution'}, xlabel='Propensity', ylabel='Probability density'>"
+       "Text(0.5, 1.0, 'IPW weights (log y)')"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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DZGdn66WXXtLnn3+u2tpaRUdHa+nSpZo9e7a3S+tQlZWVWrRokfbs2aPTp0+rV69e+s53vqOnn35aw4cPv+bnHQ6HevfurdmzZysnJ0fduvH/kfAthBMAAGAU5pwAAACjEE4AAIBRbHchs66uTidOnFCfPn140BUAADZhWZbOnz+viIiIBvdZaortwsmJEycUGRnp7TIAAEAblJWVtfgwTsmG4aRPnz6Srn654OBgL1cDAABao6qqSpGRke7f8ZbYLpzUX8oJDg4mnAAAYDOtmZLBhFgAAGAUwgkAADAK4QQAABiFcAIAAIxCOAEAAEYhnAAAAKMQTgAAgFEIJwAAwCiEEwAAYBTCCQAAMArhBAAAGIVwAgAAjEI4AQAARrHdU4mBriZqWa5H+3/+9PQOqgQAzMDICQAAMArhBAAAGKXTw8lXX32lsWPHKjY2VqNHj9aLL77Y2SUAAACDdfqckz59+ig/P189e/ZUdXW1Ro8erVmzZql///6dXQpgS57MUWF+CgA76vRw4u/vr549e0qSampqZFmWLMvq7DKADuXpJFcAwP/z+LJOfn6+ZsyYoYiICDkcDm3btq3RPi6XS1FRUQoKCtK4ceO0b9++Btu/+uorxcTEaMiQIXrssccUGhra5i8AAAC6Fo/DSXV1tWJiYuRyuZrcvnnzZjmdTq1atUoHDhxQTEyMUlJSdOrUKfc+ffv21aFDh3T8+HFt3LhRFRUVzZ6vpqZGVVVVDV4AAKDr8jicTJs2TU899ZRmzpzZ5PasrCwtXLhQaWlpio6OVk5Ojnr27KmXX3650b5hYWGKiYlRQUFBs+fLzMxUSEiI+xUZGelpyQAAwEbadbXOpUuXVFxcrOTk5P8/gZ+fkpOTVVhYKEmqqKjQ+fPnJUmVlZXKz8/XiBEjmj3m8uXLVVlZ6X6VlZW1Z8kAAMAw7Toh9syZM6qtrVVYWFiD9rCwMB0+fFiS9MUXX2jRokXuibA/+tGPNGbMmGaPGRgYqMDAwPYsEwAAGKzTV+skJCSopKSks08LAABsol0v64SGhsrf37/RBNeKigqFh4e356kAAEAX1a4jJwEBAYqLi1NeXp7uvfdeSVJdXZ3y8vKUkZFxXcd2uVxyuVyqra1th0oBz3HvEgDoHB6HkwsXLujo0aPu98ePH1dJSYn69eunoUOHyul0KjU1VWPHjlVCQoKys7NVXV2ttLS06yo0PT1d6enpqqqqUkhIyHUdCwAAmMvjcLJ//35NmTLF/d7pdEqSUlNTtX79es2ePVunT5/WypUrdfLkScXGxmr79u2NJskCAAA0xWHZ7N7x9SMnlZWVCg4O9nY58CF2vKzDs3UAmMKT3+9OfyoxAABASwgnAADAKJ1+n5O2YrUO0LE8uWzF5SIAHck2Iyfp6ekqLS1VUVGRt0sBAAAdyDbhBAAA+AbbXNYBOoIdV+AAQFfHyAkAADAK4QQAABjFNuHE5XIpOjpa8fHx3i4FAAB0INuEE1brAADgG2wTTgAAgG8gnAAAAKMQTgAAgFEIJwAAwCjchA2Axzy9eR3P4gHgCduMnLCUGAAA32CbcMJSYgAAfINtwgkAAPANhBMAAGAUwgkAADAK4QQAABiFcAIAAIxCOAEAAEYhnAAAAKPYJpxwEzYAAHyDbcIJN2EDAMA38GwdoAvz9Bk4AGACwgm6FH6MAcD+CCcwHoEDAHyLbeacAAAA30A4AQAARiGcAAAAoxBOAACAUQgnAADAKIQTAABgFNuEE25fDwCAb3BYlmV5uwhPVFVVKSQkRJWVlQoODvZ2OegE3OfEt3z+9HRvlwCgA3jy+22bkRMAAOAbCCcAAMAohBMAAGAUwgkAADAK4QQAABiFcAIAAIxCOAEAAEYhnAAAAKMQTgAAgFEIJwAAwCiEEwAAYBTbhBMe/AcAgG+wTThJT09XaWmpioqKvF0KAADoQLYJJwAAwDd083YBANBWUctyPdr/86end1AlANoT4QRe4emPCgDAdxBOABiF4AqAOScAAMAohBMAAGAUwgkAADAK4QQAABiFcAIAAIzCah0APsOTlUDcEwXwHkZOAACAUQgnAADAKIQTAABgFMIJAAAwCuEEAAAYhXACAACMQjgBAABGIZwAAACj2CacuFwuRUdHKz4+3tulAACADmSbcJKenq7S0lIVFRV5uxQAANCBbBNOAACAb+DZOgDQBJ7DA3gPIycAAMAohBMAAGAUwgkAADAK4QQAABiFcAIAAIxCOAEAAEYhnAAAAKMQTgAAgFEIJwAAwCiEEwAAYBRuX4924cmtvgEAaAkjJwAAwCiEEwAAYBTCCQAAMArhBAAAGIVwAgAAjEI4AQAARiGcAAAAoxBOAACAUQgnAADAKIQTAABgFMIJAAAwCuEEAAAYpdPDSVlZmSZPnqzo6Gj9y7/8i7Zs2dLZJQAAAIN1+lOJu3XrpuzsbMXGxurkyZOKi4vT3XffrV69enV2KQAAwECdHk4GDRqkQYMGSZLCw8MVGhqqL7/8knACAAAkteGyTn5+vmbMmKGIiAg5HA5t27at0T4ul0tRUVEKCgrSuHHjtG/fviaPVVxcrNraWkVGRnpcOAAA6Jo8DifV1dWKiYmRy+VqcvvmzZvldDq1atUqHThwQDExMUpJSdGpU6ca7Pfll19q/vz5euGFF9pWOQAA6JI8vqwzbdo0TZs2rdntWVlZWrhwodLS0iRJOTk5ys3N1csvv6xly5ZJkmpqanTvvfdq2bJlSkxMbPF8NTU1qqmpcb+vqqrytGQAAGAj7Trn5NKlSyouLtby5cvdbX5+fkpOTlZhYaEkybIsLViwQFOnTtW8efOueczMzEytWbOmPctEK0Uty/V2CQAAH9SuS4nPnDmj2tpahYWFNWgPCwvTyZMnJUl79uzR5s2btW3bNsXGxio2NlYfffRRs8dcvny5Kisr3a+ysrL2LBkAABim01frTJw4UXV1da3ePzAwUIGBgR1YEQAAMEm7hpPQ0FD5+/uroqKiQXtFRYXCw8Pb81S248klks+fnt6BlQAAYLZ2vawTEBCguLg45eXludvq6uqUl5en8ePHt+epAABAF+XxyMmFCxd09OhR9/vjx4+rpKRE/fr109ChQ+V0OpWamqqxY8cqISFB2dnZqq6udq/eaSuXyyWXy6Xa2trrOo4deDoRlZEWAEBX4nE42b9/v6ZMmeJ+73Q6JUmpqalav369Zs+erdOnT2vlypU6efKkYmNjtX379kaTZD2Vnp6u9PR0VVVVKSQk5LqOBQAAzOVxOJk8ebIsy2pxn4yMDGVkZLS5KAAA4Ls6/anEAAAALSGcAAAAo9gmnLhcLkVHRys+Pt7bpQAAgA7U6TdhaysmxAIwFfcxAtqXbUZOAACAbyCcAAAAoxBOAACAUWwz5wTN43o3AKArYeQEAAAYxTbhhKXEAAD4BtuEk/T0dJWWlqqoqMjbpQAAgA5km3ACAAB8A+EEAAAYhXACAACMQjgBAABGIZwAAACj2CacsJQYAADfYJtwwlJiAAB8A7ev9zGe3OoeAABvsM3ICQAA8A2MnABAJ/J09JKHdcIXMXICAACMQjgBAABGIZwAAACjEE4AAIBRbBNOuAkbAAC+wTbhhJuwAQDgG2wTTgAAgG8gnAAAAKMQTgAAgFEIJwAAwCiEEwAAYBTCCQAAMArhBAAAGIVwAgAAjGKbcMIdYgEA8A22CSfcIRYAAN9gm3ACAAB8QzdvFwAAaB9Ry3Jbve/nT0/vwEqA68PICQAAMArhBAAAGIVwAgAAjEI4AQAARiGcAAAAoxBOAACAUQgnAADAKNznBAB8EPdEgckYOQEAAEYhnAAAAKPYJpzwVGIAAHyDbcIJTyUGAMA32CacAAAA38BqHQAwmCeraoCugpETAABgFMIJAAAwCuEEAAAYhXACAACMQjgBAABGIZwAAACjsJT4n/AwLAAAvIuREwAAYBTCCQAAMArhBAAAGIVwAgAAjEI4AQAARiGcAAAAoxBOAACAUQgnAADAKLYJJy6XS9HR0YqPj/d2KQAAoAPZJpykp6ertLRURUVF3i4FAAB0INuEEwAA4BsIJwAAwCiEEwAAYBTCCQAAMArhBAAAGIVwAgAAjNLN2wXYWdSyXG+XAAAdztP/1n3+9PQOqgS+gpETAABgFMIJAAAwCuEEAAAYhXACAACMQjgBAABGIZwAAACjEE4AAIBRCCcAAMAohBMAAGAUwgkAADAK4QQAABiFcAIAAIzCg/8AAF2SJw8s5GGFZmHkBAAAGIVwAgAAjEI4AQAARiGcAAAAoxBOAACAUQgnAADAKF4JJzNnztQNN9yg+++/3xunBwAABvPKfU4eeeQR/eAHP9Crr77qjdMDADoQ9xfB9fLKyMnkyZPVp08fb5waAAAYzuNwkp+frxkzZigiIkIOh0Pbtm1rtI/L5VJUVJSCgoI0btw47du3rz1qBQAAPsDjcFJdXa2YmBi5XK4mt2/evFlOp1OrVq3SgQMHFBMTo5SUFJ06dapNBdbU1KiqqqrBCwAAdF0eh5Np06bpqaee0syZM5vcnpWVpYULFyotLU3R0dHKyclRz5499fLLL7epwMzMTIWEhLhfkZGRbToOAACwh3adc3Lp0iUVFxcrOTn5/0/g56fk5GQVFha26ZjLly9XZWWl+1VWVtZe5QIAAAO162qdM2fOqLa2VmFhYQ3aw8LCdPjwYff75ORkHTp0SNXV1RoyZIi2bNmi8ePHN3nMwMBABQYGtmeZAADAYF5ZSvzee+9547QAAMAG2vWyTmhoqPz9/VVRUdGgvaKiQuHh4e15KgAA0EW1azgJCAhQXFyc8vLy3G11dXXKy8tr9rJNa7lcLkVHRys+Pv56ywQAAAbz+LLOhQsXdPToUff748ePq6SkRP369dPQoUPldDqVmpqqsWPHKiEhQdnZ2aqurlZaWtp1FZqenq709HRVVVUpJCTkuo4FAADM5XE42b9/v6ZMmeJ+73Q6JUmpqalav369Zs+erdOnT2vlypU6efKkYmNjtX379kaTZAEAAJricTiZPHmyLMtqcZ+MjAxlZGS0uSgAAOC7vPJsHQAAgOYQTgAAgFFsE05YrQMAgG+wTThJT09XaWmpioqKvF0KAADoQLYJJwAAwDcQTgAAgFEIJwAAwCiEEwAAYBSvPJW4LVwul1wul2pra71dCgDAC6KW5Xq7BHQS24ycsFoHAADfYJtwAgAAfAPhBAAAGIVwAgAAjEI4AQAARiGcAAAAo9gmnPDgPwAAfINtwglLiQEA8A22CScAAMA3EE4AAIBRCCcAAMAohBMAAGAUwgkAADAK4QQAABiFcAIAAIzSzdsFtJbL5ZLL5VJtba23SwEAtJOoZbneLgEGss3ICTdhAwDAN9gmnAAAAN9AOAEAAEYhnAAAAKMQTgAAgFEIJwAAwCiEEwAAYBTCCQAAMArhBAAAGIVwAgAAjGKbcOJyuRQdHa34+HhvlwIAADqQbcIJt68HAMA32CacAAAA30A4AQAARiGcAAAAoxBOAACAUQgnAADAKIQTAABgFMIJAAAwCuEEAAAYhXACAACMQjgBAABGIZwAAACjdPN2Aa3lcrnkcrlUW1vr7VIAAF1M1LLcVu/7+dPTO7CSjmG372ebkRMe/AcAgG+wTTgBAAC+gXACAACMQjgBAABGIZwAAACjEE4AAIBRCCcAAMAohBMAAGAUwgkAADAK4QQAABiFcAIAAIxCOAEAAEYhnAAAAKMQTgAAgFEIJwAAwCiEEwAAYBTCCQAAMArhBAAAGIVwAgAAjGKbcOJyuRQdHa34+HhvlwIAADqQbcJJenq6SktLVVRU5O1SAABAB7JNOAEAAL6BcAIAAIxCOAEAAEYhnAAAAKMQTgAAgFEIJwAAwCiEEwAAYBTCCQAAMArhBAAAGIVwAgAAjEI4AQAARiGcAAAAoxBOAACAUQgnAADAKIQTAABgFMIJAAAwCuEEAAAYhXACAACMQjgBAABGIZwAAACjEE4AAIBRCCcAAMAohBMAAGAUwgkAADAK4QQAABjFK+HkT3/6k0aMGKFbbrlFL730kjdKAAAAhurW2Se8cuWKnE6ndu7cqZCQEMXFxWnmzJnq379/Z5cCAAAM1OkjJ/v27dOoUaM0ePBg9e7dW9OmTdOOHTs6uwwAAGAoj8NJfn6+ZsyYoYiICDkcDm3btq3RPi6XS1FRUQoKCtK4ceO0b98+97YTJ05o8ODB7veDBw9WeXl526oHAABdjsfhpLq6WjExMXK5XE1u37x5s5xOp1atWqUDBw4oJiZGKSkpOnXqVJsKrKmpUVVVVYMXAADoujyeczJt2jRNmzat2e1ZWVlauHCh0tLSJEk5OTnKzc3Vyy+/rGXLlikiIqLBSEl5ebkSEhKaPV5mZqbWrFnjaZkAAHhd1LJcj/b//OnpHXZsO2nXOSeXLl1ScXGxkpOT//8Efn5KTk5WYWGhJCkhIUEff/yxysvLdeHCBb399ttKSUlp9pjLly9XZWWl+1VWVtaeJQMAAMO062qdM2fOqLa2VmFhYQ3aw8LCdPjw4asn7NZNa9eu1ZQpU1RXV6elS5e2uFInMDBQgYGB7VkmAAAwWKcvJZake+65R/fcc483Tg0AAAzXrpd1QkND5e/vr4qKigbtFRUVCg8Pb89TAQCALqpdw0lAQIDi4uKUl5fnbqurq1NeXp7Gjx9/Xcd2uVyKjo5WfHz89ZYJAAAM5vFlnQsXLujo0aPu98ePH1dJSYn69eunoUOHyul0KjU1VWPHjlVCQoKys7NVXV3tXr3TVunp6UpPT1dVVZVCQkKu61gAAMBcHoeT/fv3a8qUKe73TqdTkpSamqr169dr9uzZOn36tFauXKmTJ08qNjZW27dvbzRJFgAAoCkeh5PJkyfLsqwW98nIyFBGRkabiwIAAL7LK08lBgAAaA7hBAAAGMU24YTVOgAA+AbbhJP09HSVlpaqqKjI26UAAIAOZJtwAgAAfAPhBAAAGIVwAgAAjOKVB/9dj/p7rFRVVXXI8etq/tEhxwUAdA2e/P54+pvSkcfuiBractxr3StNkhxWa/YyyN/+9jdFRkZ6uwwAANAGZWVlGjJkSIv72C6c1NXV6cSJE+rTp48cDkez+1VVVSkyMlJlZWUKDg7uxAp9B33cOejnzkE/dw76uXOY2M+WZen8+fOKiIiQn1/Ls0psd1nHz8/vmonrm4KDg435w3RV9HHnoJ87B/3cOejnzmFaP7f2wb1MiAUAAEYhnAAAAKN02XASGBioVatWKTAw0NuldFn0ceegnzsH/dw56OfOYfd+tt2EWAAA0LV12ZETAABgT4QTAABgFMIJAAAwCuEEAAAYhXACAACMYutw4nK5FBUVpaCgII0bN0779u1rcf/s7GyNGDFCPXr0UGRkpB599FF9/fXXnVStPXnSx5cvX9bPfvYzDR8+XEFBQYqJidH27ds7sVp7ys/P14wZMxQRESGHw6Ft27Zd8zO7du3SbbfdpsDAQN18881av359h9dpd57289///nd973vf07e+9S35+fnpxz/+cafUaXee9vPWrVt1xx13aMCAAQoODtb48eP1zjvvdE6xNuVpH+/evVsTJkxQ//791aNHD40cOVL/+Z//2TnFtpFtw8nmzZvldDq1atUqHThwQDExMUpJSdGpU6ea3H/jxo1atmyZVq1apU8++UT//d//rc2bN+snP/lJJ1duH5728YoVK/T888/rN7/5jUpLS7VkyRLNnDlTBw8e7OTK7aW6uloxMTFyuVyt2v/48eOaPn26pkyZopKSEv34xz/WQw89xH/Qr8HTfq6pqdGAAQO0YsUKxcTEdHB1XYen/Zyfn6877rhDf/7zn1VcXKwpU6ZoxowZ/HejBZ72ca9evZSRkaH8/Hx98sknWrFihVasWKEXXnihgyu9DpZNJSQkWOnp6e73tbW1VkREhJWZmdnk/unp6dbUqVMbtDmdTmvChAkdWqededrHgwYNsp599tkGbbNmzbK+//3vd2idXYkk66233mpxn6VLl1qjRo1q0DZ79mwrJSWlAyvrWlrTz9+UlJRkPfLIIx1WT1flaT/Xi46OttasWdP+BXVBbe3jmTNnWnPnzm3/gtqJLUdOLl26pOLiYiUnJ7vb/Pz8lJycrMLCwiY/k5iYqOLiYvdliWPHjunPf/6z7r777k6p2W7a0sc1NTUKCgpq0NajRw/t3r27Q2v1NYWFhQ3+LpKUkpLS7N8FsJO6ujqdP39e/fr183YpXdbBgwf1wQcfKCkpydulNMt2TyWWpDNnzqi2tlZhYWEN2sPCwnT48OEmP/O9731PZ86c0cSJE2VZlq5cuaIlS5ZwWacZbenjlJQUZWVl6fbbb9fw4cOVl5enrVu3qra2tjNK9hknT55s8u9SVVWlixcvqkePHl6qDLh+v/71r3XhwgU9+OCD3i6lyxkyZIhOnz6tK1euaPXq1XrooYe8XVKzbDly0ha7du3SL37xCz333HM6cOCAtm7dqtzcXD355JPeLq3LWLdunW655RaNHDlSAQEBysjIUFpamvz8fOZfMwDXYePGjVqzZo3efPNNDRw40NvldDkFBQXav3+/cnJylJ2drU2bNnm7pGbZcuQkNDRU/v7+qqioaNBeUVGh8PDwJj/zxBNPaN68ee6kOGbMGFVXV2vRokX66U9/yg/oP2lLHw8YMEDbtm3T119/rbNnzyoiIkLLli3TTTfd1Bkl+4zw8PAm/y7BwcGMmsC2fvvb3+qhhx7Sli1bGl22RPsYNmyYpKu/fxUVFVq9erXmzJnj5aqaZstf5ICAAMXFxSkvL8/dVldXp7y8PI0fP77Jz/zjH/9oFED8/f0lSRbPPmykLX1cLygoSIMHD9aVK1f0u9/9Tt/97nc7ulyfMn78+AZ/F0l69913r/l3AUy1adMmpaWladOmTZo+fbq3y/EJdXV1qqmp8XYZzbLlyIkkOZ1OpaamauzYsUpISFB2draqq6uVlpYmSZo/f74GDx6szMxMSdKMGTOUlZWlb3/72xo3bpyOHj2qJ554QjNmzHCHFDTkaR9/+OGHKi8vV2xsrMrLy7V69WrV1dVp6dKl3vwaxrtw4YKOHj3qfn/8+HGVlJSoX79+Gjp0qJYvX67y8nK99tprkqQlS5bo2Wef1dKlS/WDH/xA77//vt58803l5uZ66yvYgqf9LEklJSXuz54+fVolJSUKCAhQdHR0Z5dvG57288aNG5Wamqp169Zp3LhxOnnypKSrk+lDQkK88h1M52kfu1wuDR06VCNHjpR0dfn2r3/9az388MNeqb9VvL1c6Hr85je/sYYOHWoFBARYCQkJ1t69e93bkpKSrNTUVPf7y5cvW6tXr7aGDx9uBQUFWZGRkdYPf/hD69y5c51fuI140se7du2ybr31ViswMNDq37+/NW/ePKu8vNwLVdvLzp07LUmNXvV9m5qaaiUlJTX6TGxsrBUQEGDddNNN1iuvvNLpddtNW/q5qf1vvPHGTq/dTjzt56SkpBb3R2Oe9vEzzzxjjRo1yurZs6cVHBxsffvb37aee+45q7a21jtfoBUclsU1DQAAYA5bzjkBAABdF+EEAAAYhXACAACMQjgBAABGIZwAAACjEE4AAIBRCCcAAMAohBMAAGAUwgkAADAK4QQAABiFcAIAAIzyf8ptxPLBGmukAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2506,64 +2524,246 @@
     }
    ],
    "source": [
-    "res = evaluate(weight_model, X, T, Y, cv=\"auto\")\n",
-    "res.plot_weight_distribution(phase=\"valid\", reflect=False, norm_hist=True)"
+    "plt.figure(); plt.hist(w, bins=40); plt.yscale('log'); plt.title('IPW weights (log y)')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "OOF로 검증해도 Propensity Score의 Overlap 및 Positivity가 양호한 것을 확인"
+    "결과 해석\n",
+    "1) ESS와 N수가 거의 비슷한 것을 확인할 수 있음(VIF ≈ 1) -> 가중치가 고르게 퍼져있다\n",
+    "2) 히스토그램이 1 주변에 모여있고(= 가중치 과도하게 쏠리지 않음), 꼬리 부분도 완만(= 극단 가중치 거의 없음)\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### ROC Curve"
+    "다만 VIF와 Propensity Score Overlap이 정량적으로 얼마나 되어야 한다 라는 지표는 찾을 수 없었습니다.    \n",
+    "\n",
+    "\n",
+    "데이터 및 분석의 맥락에 따라 다르게 지정하되, propensity score의 overlap이 약하다면 위에서 진행한 clipping 등의 방법을 적용해보면 좋습니다. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## AIPW & DR에 대한 Robustness Check 흐름"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "AIPW에는 IPW뿐만 아니라 Outcome에 대한 Term이 포함되기 때문에    \n",
+    "**위에서 진행한 IPW에 대한 Robustness Check뿐만 아니라 Outcome에 대한 Robustness Check 및 AIPW 자체에 대한 검사도 함께 진행해야 합니다.**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from causallib.estimation.ipw import IPW\n",
+    "from causallib.estimation.doubly_robust import AIPW\n",
+    "from causallib.estimation.standardization import Standardization\n",
+    "from sklearn.linear_model import LogisticRegression, Ridge\n",
+    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
+    "from causallib.estimation import TMLE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "출처: https://causallib.readthedocs.io/en/latest/causallib.estimation.doubly_robust.html?utm_source=chatgpt.com"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1) (필수) 부트스트랩 신뢰구간 — AIPW ATE의 추론 견고성"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'ROC Curve'}, xlabel='False Positive Rate', ylabel='True Positive Rate'>"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AIPW ATE 95% bootstrap CI: [0.4139, 0.4879]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def _aipw_ate_once(Xb, Tb, Yb):\n",
+    "    om = Standardization(learner=Ridge(alpha=1.0))\n",
+    "    wm = IPW(learner=LogisticRegression(max_iter=1000),\n",
+    "             clip_min=0.01, clip_max=0.99, use_stabilized=True)\n",
+    "    dr_b = AIPW(outcome_model=om, weight_model=wm)\n",
+    "    dr_b.fit(Xb, Tb, Yb)\n",
+    "    mu_b = dr_b.estimate_population_outcome(Xb, Tb, Yb)\n",
+    "    return float(dr_b.estimate_effect(mu_b[1], mu_b[0], agg=\"population\")[\"diff\"])\n",
+    "\n",
+    "B = 500\n",
+    "rng = np.random.default_rng(42)\n",
+    "n = len(Y)\n",
+    "ates = []\n",
+    "for b in range(B):\n",
+    "    idx = rng.integers(0, n, n)     # 부트스트랩 인덱스\n",
+    "    ates.append(_aipw_ate_once(X.iloc[idx], T.iloc[idx], Y.iloc[idx]))\n",
+    "\n",
+    "lo, hi = np.percentile(ates, [2.5, 97.5])\n",
+    "print(f\"AIPW ATE 95% bootstrap CI: [{lo:.4f}, {hi:.4f}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2) DR 스트레스 테스트 — 결과모형 및 PS모형 각각 하나씩 변경해보기 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "위에서 말했듯 DR에서는 결과모형, PS모형 모두 사용하고 둘 중 하나만 맞으면 결과값이 안정적이라는 장점이 있습니다.     \n",
+    "이러한 DR의 특성을 사용해서 각각의 모형을 하나씩만 바꿨을 때 ATE가 크게 변동이 없으면 모델의 안정성이 확보되는 것으로 해석할 수 있습니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_om = Standardization(learner=Ridge(alpha=1.0))\n",
+    "base_wm = IPW(learner=LogisticRegression(max_iter=1000),\n",
+    "              clip_min=0.01, clip_max=0.99, use_stabilized=True)\n",
+    "dr_base = AIPW(outcome_model=base_om, weight_model=base_wm).fit(X, T, Y)\n",
+    "mu_base = dr_base.estimate_population_outcome(X, T, Y)\n",
+    "ate_base = float(dr_base.estimate_effect(mu_base[1], mu_base[0], agg=\"population\")[\"diff\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* 결과 모형만 교체: 비선형 예측기(Random Forest)로 변경"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "altY_om = Standardization(learner=RandomForestRegressor(n_estimators=400, min_samples_leaf=5, random_state=0))\n",
+    "dr_altY = AIPW(outcome_model=altY_om, weight_model=base_wm).fit(X, T, Y)\n",
+    "mu_altY = dr_altY.estimate_population_outcome(X, T, Y)\n",
+    "ate_altY = float(dr_altY.estimate_effect(mu_altY[1], mu_altY[0], agg=\"population\")[\"diff\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* PS모형만 교체: 비선형 분류기 (RF)로 변경"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "altPS_wm = IPW(learner=RandomForestClassifier(n_estimators=400, min_samples_leaf=5, random_state=0),\n",
+    "               clip_min=0.01, clip_max=0.99, use_stabilized=True)\n",
+    "dr_altPS = AIPW(outcome_model=base_om, weight_model=altPS_wm).fit(X, T, Y)\n",
+    "mu_altPS = dr_altPS.estimate_population_outcome(X, T, Y)\n",
+    "ate_altPS = float(dr_altPS.estimate_effect(mu_altPS[1], mu_altPS[0], agg=\"population\")[\"diff\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
     {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'AIPW_ATE_base': 0.45041234661005924, 'AIPW_ATE_altY_only': 0.4393682311294207, 'AIPW_ATE_altPS_only': 0.4481943633726966}\n"
+     ]
     }
    ],
    "source": [
-    "res.plot_roc_curve(phase=\"valid\")"
+    "print({\n",
+    "    \"AIPW_ATE_base\": ate_base,\n",
+    "    \"AIPW_ATE_altY_only\": ate_altY,\n",
+    "    \"AIPW_ATE_altPS_only\": ate_altPS\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ATE의 결과가 변동성이 거의 없는 것을 확인할 수 있습니다.   \n",
+    "-> DR 모형의 안정성 확보"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "위의 그래프는 우리가 구한 Propensity Score의 Overlap 정도를 확인하기 위한 ROC Curve이다(AUC가 0.5에 가까울수록 as-if random).    \n",
-    "- Propensity: 가중치 없이 Propensity Score 자체만으로 보았을 때\n",
-    "- Weighted: IPW 가중을 걸었을 때 -> 만약 Propensity Score와의 차이가 크면 극단 PS에 의해 분산 및 불안정성이 커진 신호\n",
-    "- Expected: 무작위 배정 레퍼런스 -> 위 두 경우의 AUC가 Expected와 크게 다르면 PS가 과도하게 극단이거나 표본 불균형 존재\n",
+    "3) TMLE 교차확인 — 같은 DR 계열 추정과 합치하는지 여부 확인"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TMLE ATE (cross-check): 0.4391710324031751\n",
+      "AIPW ATE (baseline):    0.45041234661005924\n"
+     ]
+    }
+   ],
+   "source": [
+    "om_tml = Standardization(\n",
+    "    learner=RandomForestRegressor(n_estimators=400, min_samples_leaf=5, random_state=0)\n",
+    ")\n",
+    "wm_tml = IPW(\n",
+    "    learner=LogisticRegression(max_iter=1000, solver='lbfgs'),\n",
+    "    clip_min=0.01, clip_max=0.99,\n",
+    "    use_stabilized=True\n",
+    ")\n",
+    "\n",
+    "tmle = TMLE(outcome_model=om_tml, weight_model=wm_tml)\n",
+    "tmle.fit(X, T, Y)\n",
+    "\n",
+    "# 핵심: 잠재결과부터 추정\n",
+    "mu_tmle = tmle.estimate_population_outcome(X, T, Y)   # dict 형태, 보통 {0: μ0, 1: μ1}\n",
+    "\n",
+    "# 그 다음 효과 계산\n",
+    "ate_tmle = tmle.estimate_effect(mu_tmle[1], mu_tmle[0], agg=\"population\")[\"diff\"]\n",
     "\n",
-    "그래프 해석: Facure의 책에서 Treatment가 완전 무작위가 아니라고 밝혔다. 하지만, 우리가 잡은 \"X 기준으로 보았을 때(Given X)\"는 ROC Curve로 본 Treatment는 거의 랜덤에 준한다고 판단할 수 있다."
+    "print(\"TMLE ATE (cross-check):\", float(ate_tmle))\n",
+    "print(\"AIPW ATE (baseline):   \", float(ate_base))"
    ]
   },
   {

From 37004ceaeaade05537119265f94eec2837479084 Mon Sep 17 00:00:00 2001
From: MinSeok Kang <rkdals1148@gmail.com>
Date: Sun, 12 Oct 2025 17:37:01 +0900
Subject: [PATCH 4/6] feat(ate): update propensity_score_and_dml notebook

---
 book/ate/propensity_score_and_dml.ipynb | 1069 +++--------------------
 1 file changed, 102 insertions(+), 967 deletions(-)

diff --git a/book/ate/propensity_score_and_dml.ipynb b/book/ate/propensity_score_and_dml.ipynb
index c93a5bf..e30da7c 100644
--- a/book/ate/propensity_score_and_dml.ipynb
+++ b/book/ate/propensity_score_and_dml.ipynb
@@ -211,7 +211,7 @@
     }
    ],
    "source": [
-    "data = pd.read_csv(\"./data/learning_mindset.csv\")\n",
+    "data = pd.read_csv(\"../data/matheus_data/learning_mindset.csv\")\n",
     "data.sample(5, random_state=5)"
    ]
   },
@@ -291,8 +291,8 @@
      "output_type": "stream",
      "text": [
       "Original Sample Size 10391\n",
-      "Treated Population Sample Size 10387.611484606225\n",
-      "Untreated Population Sample Size 10391.506082909109\n"
+      "Treated Population Sample Size 10387.611324207002\n",
+      "Untreated Population Sample Size 10391.506162305861\n"
      ]
     }
    ],
@@ -313,9 +313,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Y1: 0.25981028516541704\n",
-      "Y0: -0.12903052853557984\n",
-      "ATE 0.38884081370099727\n"
+      "Y1: 0.25981027799629486\n",
+      "Y0: -0.12903052783749974\n",
+      "ATE 0.38884080583379527\n"
      ]
     }
    ],
@@ -361,7 +361,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.38884081370099727\n"
+      "0.38884080583379527\n"
      ]
     }
    ],
@@ -466,8 +466,7 @@
       "text/html": [
        "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -612,21 +611,12 @@
        "/* Toggleable label */\n",
        "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: flex;\n",
+       "  display: block;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
        "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
@@ -644,7 +634,9 @@
        "/* Toggleable content - dropdown */\n",
        "\n",
        "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  display: none;\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
@@ -670,9 +662,9 @@
        "\n",
        "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
        "}\n",
        "\n",
        "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
@@ -777,8 +769,7 @@
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
+       "  margin-left: 1ex;\n",
        "  /* unfitted */\n",
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-unfitted-level-1);\n",
@@ -877,433 +868,20 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
        "                learner=Ridge()), overlap_weighting=False, predict_proba=False, weight_covariates=None,\n",
-       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
-       "    learner=LogisticRegression(max_iter=1000)))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>AIPW</div></div><div><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('outcome_model',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">outcome_model&nbsp;</td>\n",
-       "            <td class=\"value\">Standardizati...arner=Ridge())</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('weight_model',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">weight_model&nbsp;</td>\n",
-       "            <td class=\"value\">IPW(_doc_link...ax_iter=1000))</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('outcome_covariates',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">outcome_covariates&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('weight_covariates',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">weight_covariates&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('overlap_weighting',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">overlap_weighting&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>outcome_model: Standardization</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__\"><pre>Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
-       "                learner=Ridge())</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>learner: Ridge</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__learner__\"><pre>Ridge()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Ridge</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__learner__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">alpha&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy_X&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">positive&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>weight_model: IPW</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__\"><pre>IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
-       "    learner=LogisticRegression(max_iter=1000))</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>learner: LogisticRegression</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__learner__\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__learner__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">penalty&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">1000</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">multi_class&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
+       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000)))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;AIPW<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "                learner=Ridge()), overlap_weighting=False, predict_proba=False, weight_covariates=None,\n",
+       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000)))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">outcome_model: Standardization</label><div class=\"sk-toggleable__content \"><pre>Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "                learner=Ridge())</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">learner: Ridge</label><div class=\"sk-toggleable__content \"><pre>Ridge()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;Ridge<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a></label><div class=\"sk-toggleable__content \"><pre>Ridge()</pre></div> </div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">weight_model: IPW</label><div class=\"sk-toggleable__content \"><pre>IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000))</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">learner: LogisticRegression</label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
       ],
       "text/plain": [
-       "AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
        "                learner=Ridge()), overlap_weighting=False, predict_proba=False, weight_covariates=None,\n",
-       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
        "    learner=LogisticRegression(max_iter=1000)))"
       ]
      },
@@ -1326,9 +904,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "μ1 (A=1): 0.3032993079280466\n",
-      "μ0 (A=0): -0.14711303868200887\n",
-      "ATE (DR, vanilla): 0.45041234661005547\n"
+      "μ1 (A=1): 0.30329930792804977\n",
+      "μ0 (A=0): -0.1471130386820095\n",
+      "ATE (DR, vanilla): 0.45041234661005924\n"
      ]
     }
    ],
@@ -1366,8 +944,7 @@
       "text/html": [
        "<style>#sk-container-id-2 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
        "  /* Definition of color scheme for unfitted estimators */\n",
        "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
@@ -1512,21 +1089,12 @@
        "/* Toggleable label */\n",
        "#sk-container-id-2 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
-       "  display: flex;\n",
+       "  display: block;\n",
        "  width: 100%;\n",
        "  margin-bottom: 0;\n",
        "  padding: 0.5em;\n",
        "  box-sizing: border-box;\n",
        "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
        "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
@@ -1544,7 +1112,9 @@
        "/* Toggleable content - dropdown */\n",
        "\n",
        "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  display: none;\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
        "  text-align: left;\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
@@ -1570,9 +1140,9 @@
        "\n",
        "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
        "}\n",
        "\n",
        "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
@@ -1677,8 +1247,7 @@
        "  height: 1em;\n",
        "  width: 1em;\n",
        "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
+       "  margin-left: 1ex;\n",
        "  /* unfitted */\n",
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-unfitted-level-1);\n",
@@ -1777,433 +1346,20 @@
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "                learner=Ridge()), overlap_weighting=True, predict_proba=False, weight_covariates=None,\n",
+       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000)))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;AIPW<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
        "                learner=Ridge()), overlap_weighting=True, predict_proba=False, weight_covariates=None,\n",
-       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
-       "    learner=LogisticRegression(max_iter=1000)))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>AIPW</div></div><div><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('outcome_model',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">outcome_model&nbsp;</td>\n",
-       "            <td class=\"value\">Standardizati...arner=Ridge())</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('weight_model',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">weight_model&nbsp;</td>\n",
-       "            <td class=\"value\">IPW(_doc_link...ax_iter=1000))</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('outcome_covariates',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">outcome_covariates&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('weight_covariates',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">weight_covariates&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('overlap_weighting',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">overlap_weighting&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>outcome_model: Standardization</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__\"><pre>Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
-       "                learner=Ridge())</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>learner: Ridge</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__learner__\"><pre>Ridge()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Ridge</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"outcome_model__learner__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">alpha&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy_X&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">positive&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>weight_model: IPW</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__\"><pre>IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
-       "    learner=LogisticRegression(max_iter=1000))</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>learner: LogisticRegression</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__learner__\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"weight_model__learner__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">penalty&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">dual&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">C&nbsp;</td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_iter&nbsp;</td>\n",
-       "            <td class=\"value\">1000</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">multi_class&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "</script></body>"
+       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000)))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">outcome_model: Standardization</label><div class=\"sk-toggleable__content \"><pre>Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "                learner=Ridge())</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">learner: Ridge</label><div class=\"sk-toggleable__content \"><pre>Ridge()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;Ridge<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a></label><div class=\"sk-toggleable__content \"><pre>Ridge()</pre></div> </div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">weight_model: IPW</label><div class=\"sk-toggleable__content \"><pre>IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "    learner=LogisticRegression(max_iter=1000))</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">learner: LogisticRegression</label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
       ],
       "text/plain": [
-       "AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
+       "AIPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, outcome_covariates=None, outcome_model=Standardization(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, encode_treatment=False, predict_proba=False,\n",
        "                learner=Ridge()), overlap_weighting=True, predict_proba=False, weight_covariates=None,\n",
-       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.7/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
+       "     weight_model=IPW(_doc_link_module=sklearn, _doc_link_template=https://scikit-learn.org/1.5/modules/generated/{estimator_module}.{estimator_name}.html, _doc_link_url_param_generator=None, clip_max=0.99, clip_min=0.01, use_stabilized=True, verbose=False,\n",
        "    learner=LogisticRegression(max_iter=1000)))"
       ]
      },
@@ -2228,7 +1384,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " ATE(DR, overlap-weighting): 0.34674117821712797\n"
+      " ATE(DR, overlap-weighting): 0.3467411782171299\n"
      ]
     }
    ],
@@ -2248,7 +1404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -2317,7 +1473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2335,7 +1491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -2344,13 +1500,13 @@
        "<Axes: xlabel='Absolute Standard Mean Difference', ylabel='Covariates'>"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2360,6 +1516,7 @@
     }
    ],
    "source": [
+    "res = evaluate(weight_model, X, T, Y, cv=\"auto\")\n",
     "res.plot_covariate_balance(kind=\"love\", phase=\"valid\", thresh=0.1)"
    ]
   },
@@ -2381,7 +1538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -2390,13 +1547,13 @@
        "<Axes: title={'center': 'Propensity Distribution'}, xlabel='Propensity', ylabel='Probability density'>"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2432,7 +1589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2441,13 +1598,13 @@
        "<Axes: title={'center': 'Propensity Distribution'}, xlabel='Propensity', ylabel='Probability density'>"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2457,7 +1614,6 @@
     }
    ],
    "source": [
-    "res = evaluate(weight_model, X, T, Y, cv=\"auto\")\n",
     "res.plot_weight_distribution(phase=\"valid\", reflect=False, norm_hist=True)"
    ]
   },
@@ -2477,7 +1633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -2499,7 +1655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -2508,7 +1664,7 @@
        "Text(0.5, 1.0, 'IPW weights (log y)')"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2564,7 +1720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2587,12 +1743,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "1) (필수) 부트스트랩 신뢰구간 — AIPW ATE의 추론 견고성"
+    "- 추론 자체의 견고성 확인    \n",
+    ": 부트스트랩 신뢰구간을 우선적으로 구하여 DR ATE 추정치의 표본 불확실성을 먼저 평가해봅시다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2629,7 +1786,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "2) DR 스트레스 테스트 — 결과모형 및 PS모형 각각 하나씩 변경해보기 "
+    "부트스트랩으로 구한 ATE의 CI 구간이 0을 포함하지 않고, 그 구간의 범위도 넓지 않아 신빙성 있게 ATE를 구한 것을 확인할 수 있습니다.     \n",
+    "\n",
+    "다음으로 DR모델 자체의 강건성 검증을 해보도록 하겠습니다."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- DR 스트레스 테스트 — 결과모형 및 PS모형 각각 하나씩 변경해보기 "
    ]
   },
   {
@@ -2637,12 +1803,14 @@
    "metadata": {},
    "source": [
     "위에서 말했듯 DR에서는 결과모형, PS모형 모두 사용하고 둘 중 하나만 맞으면 결과값이 안정적이라는 장점이 있습니다.     \n",
-    "이러한 DR의 특성을 사용해서 각각의 모형을 하나씩만 바꿨을 때 ATE가 크게 변동이 없으면 모델의 안정성이 확보되는 것으로 해석할 수 있습니다."
+    "\n",
+    "\n",
+    "이러한 DR의 특성을 사용해서 각각의 모형을 하나씩만 바꿨을 때 ATE가 크게 변동이 없으면 모델의 안정성이 확보되는 것으로 해석할 수 있습니다. (Knaus et al. (2021))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2658,18 +1826,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* 결과 모형만 교체: 비선형 예측기(Random Forest)로 변경"
+    "* 1) 결과 모형만 교체: 비선형 예측기(Random Forest)로 변경"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
     "altY_om = Standardization(learner=RandomForestRegressor(n_estimators=400, min_samples_leaf=5, random_state=0))\n",
     "dr_altY = AIPW(outcome_model=altY_om, weight_model=base_wm).fit(X, T, Y)\n",
     "mu_altY = dr_altY.estimate_population_outcome(X, T, Y)\n",
+    "\n",
+    "\n",
     "ate_altY = float(dr_altY.estimate_effect(mu_altY[1], mu_altY[0], agg=\"population\")[\"diff\"])"
    ]
   },
@@ -2677,12 +1847,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* PS모형만 교체: 비선형 분류기 (RF)로 변경"
+    "* 2) PS모형만 교체: 비선형 분류기 (RF)로 변경"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2690,12 +1860,21 @@
     "               clip_min=0.01, clip_max=0.99, use_stabilized=True)\n",
     "dr_altPS = AIPW(outcome_model=base_om, weight_model=altPS_wm).fit(X, T, Y)\n",
     "mu_altPS = dr_altPS.estimate_population_outcome(X, T, Y)\n",
+    "\n",
+    "\n",
     "ate_altPS = float(dr_altPS.estimate_effect(mu_altPS[1], mu_altPS[0], agg=\"population\")[\"diff\"])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Y와 Propensity Score를 구할 때 위에서는 Ridge로 구했었지만, 이번에는 학습기의 종류를 바꾸어 학습기의 교체로 인해 ATE가 크게 흔들리는지 확인해봅시다.   "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -2718,54 +1897,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ATE의 결과가 변동성이 거의 없는 것을 확인할 수 있습니다.   \n",
+    "예측기의 종류에 따라 ATE의 결과가 변동성이 거의 없는 것을 확인할 수 있습니다.   \n",
     "-> DR 모형의 안정성 확보"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "3) TMLE 교차확인 — 같은 DR 계열 추정과 합치하는지 여부 확인"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TMLE ATE (cross-check): 0.4391710324031751\n",
-      "AIPW ATE (baseline):    0.45041234661005924\n"
-     ]
-    }
-   ],
-   "source": [
-    "om_tml = Standardization(\n",
-    "    learner=RandomForestRegressor(n_estimators=400, min_samples_leaf=5, random_state=0)\n",
-    ")\n",
-    "wm_tml = IPW(\n",
-    "    learner=LogisticRegression(max_iter=1000, solver='lbfgs'),\n",
-    "    clip_min=0.01, clip_max=0.99,\n",
-    "    use_stabilized=True\n",
-    ")\n",
-    "\n",
-    "tmle = TMLE(outcome_model=om_tml, weight_model=wm_tml)\n",
-    "tmle.fit(X, T, Y)\n",
-    "\n",
-    "# 핵심: 잠재결과부터 추정\n",
-    "mu_tmle = tmle.estimate_population_outcome(X, T, Y)   # dict 형태, 보통 {0: μ0, 1: μ1}\n",
-    "\n",
-    "# 그 다음 효과 계산\n",
-    "ate_tmle = tmle.estimate_effect(mu_tmle[1], mu_tmle[0], agg=\"population\")[\"diff\"]\n",
-    "\n",
-    "print(\"TMLE ATE (cross-check):\", float(ate_tmle))\n",
-    "print(\"AIPW ATE (baseline):   \", float(ate_base))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2776,9 +1911,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (bayes311)",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "bayes311"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2790,7 +1925,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

From 2b37e73bfee85e75afcb7ab24e323bbde2fd2dd6 Mon Sep 17 00:00:00 2001
From: MinSeok Kang <rkdals1148@gmail.com>
Date: Sat, 18 Oct 2025 18:34:46 +0900
Subject: [PATCH 5/6] feat: update propensity_score_and_dml notebook

---
 book/ate/propensity_score_and_dml.ipynb | 417 ++++++++++++++----------
 1 file changed, 241 insertions(+), 176 deletions(-)

diff --git a/book/ate/propensity_score_and_dml.ipynb b/book/ate/propensity_score_and_dml.ipynb
index e30da7c..13c97bc 100644
--- a/book/ate/propensity_score_and_dml.ipynb
+++ b/book/ate/propensity_score_and_dml.ipynb
@@ -11,35 +11,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- Matching"
+    "- IPW, AIPW, Doubly Robust Estimator"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 질문이나 의견을 남겨주세요.\n",
-    "<script src=\"https://utteranc.es/client.js\"\n",
-    "        repo=\"CausalInferenceLab/awesome-causal-inference-python\"\n",
-    "        issue-term=\"pathname\"\n",
-    "        theme=\"github-light\"\n",
-    "        crossorigin=\"anonymous\"\n",
-    "        async>\n",
-    "</script>"
+    "### Propensity Score 추정"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- IPW, AIPW, Doubly Robust Estimator"
+    "출처: https://matheusfacure.github.io/python-causality-handbook/11-Propensity-Score.html"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "출처: https://matheusfacure.github.io/python-causality-handbook/11-Propensity-Score.html"
+    "IPW와 AIPW, Doubly Robust 모두 Propensity Score를 활용한 개념들이기 때문에 먼저 Propensity Score부터 간단하게 구해보겠습니다."
    ]
   },
   {
@@ -258,7 +251,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## IPW 생성 및 ATE 추정"
+    "## IPW 및 ATE 추정"
    ]
   },
   {
@@ -278,7 +271,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Vanilla IPW"
+    "#### 1) IPW로 가상(Pseudo) 모집단 생성    \n",
+    "\n",
+    "표본의 각 개체에 IPW 적용      \n"
    ]
   },
   {
@@ -292,7 +287,7 @@
      "text": [
       "Original Sample Size 10391\n",
       "Treated Population Sample Size 10387.611324207002\n",
-      "Untreated Population Sample Size 10391.506162305861\n"
+      "Untreated(Control) Population Sample Size 10391.506162305861\n"
      ]
     }
    ],
@@ -301,7 +296,16 @@
     "weight_nt = 1/(1-data_ps.query(\"intervention==0\")[\"propensity_score\"])\n",
     "print(\"Original Sample Size\", data.shape[0])\n",
     "print(\"Treated Population Sample Size\", sum(weight_t))\n",
-    "print(\"Untreated Population Sample Size\", sum(weight_nt))"
+    "print(\"Untreated(Control) Population Sample Size\", sum(weight_nt))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2) ATE 추정\n",
+    "\n",
+    "이제 Pseudo 집단에서의 Treat그룹과 Control그룹 각각의 Average Potential Outcome을 구하고, 이를 토대로 ATE를 추정합니다."
    ]
   },
   {
@@ -327,7 +331,7 @@
     "y0_ipw = sum(data_ps.query(\"intervention==0\")[\"achievement_score\"]*weight_nt) / len(data)\n",
     "\n",
     "ate_ipw = y1_ipw - y0_ipw\n",
-    "#ate = np.mean(weight * data_ps[\"achievement_score\"])\n",
+    "#ate = np.mean(weight * data_ps[\"achievement_score\"]) -> 이렇게도 ATE 계산 가능\n",
     "\n",
     "print(\"Y1:\", y1_ipw)\n",
     "print(\"Y0:\", y0_ipw)\n",
@@ -338,7 +342,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "결과 해석: \n",
+    "**결과 해석**: \n",
     "1. Treatment 받은 개인이 Treatment 받지 않은 동료보다 achievement_score가 0.38 표준편차 더 크다. (achievement_score는 표준화된 결과이기 때문에 표준 편차의 차이로 해석)\n",
     "2. 아무도 Treatment 받지 않은 경우 일반적인 성취 수준이 현재보다 0.12 표준편차 더 낮다.\n",
     "3. 모든 사람이 Treatment(세미나)를 받았다면 일반적인 성취 수준이 0.25 표준편차 더 높음."
@@ -352,24 +356,6 @@
     "위의 코드에 주석처리한 부분을 그대로 실행해보면 똑같은 결과를 얻을 수 있는 것을 알 수 있다."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.38884080583379527\n"
-     ]
-    }
-   ],
-   "source": [
-    "ate_ipw = np.mean(weight * data_ps[\"achievement_score\"])\n",
-    "print(ate_ipw)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -394,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -407,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -431,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -444,21 +430,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "PS Score를 구할 때 max_iter을 충분히 큰 숫자(1000)으로 설정해 수치 최적화가 수렴할 수 있도록 설정합니다.   \n",
-    "또한 클리핑을 사용하여 $ \\hat{e} $가 [0.01, 0.99]에서만 존재하도록 극단 가중치를 완화합니다.  \n",
-    "(use_stabilized = True)   "
+    "Propensity Score를 구할 때 max_iter을 충분히 큰 숫자(1000)으로 설정해 수치 최적화가 수렴할 수 있도록 설정합니다.   \n",
+    "또한 클리핑을 사용하여 $ \\hat{e} $가 [0.01, 0.99]에서만 존재하도록 극단 가중치를 완화합니다 (use_stabilized = True).  "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "위에서 구한 PS score과 IPW를 활용하여 AIPW를 구합니다. (Vanilla)"
+    "#### AIPW 추정"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "위에서 구한 PS와 IPW를 활용하여 AIPW를 구합니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -885,7 +877,7 @@
        "    learner=LogisticRegression(max_iter=1000)))"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -897,7 +889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -919,6 +911,13 @@
     "print(\"ATE (DR, vanilla):\", ate_aipw)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**결과 해석**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -936,7 +935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1363,7 +1362,7 @@
        "    learner=LogisticRegression(max_iter=1000)))"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1377,7 +1376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1404,7 +1403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1442,7 +1441,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## IPW Robustness Check의 흐름\n",
+    "## Robustness Check의 흐름\n",
     "\n",
     "각각의 수치에 대한 검증은 필수입니다.  \n",
     "어떤 수치가 더 Robust하게 추정이 된 걸까요?   \n",
@@ -1473,7 +1472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1491,7 +1490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1500,7 +1499,7 @@
        "<Axes: xlabel='Absolute Standard Mean Difference', ylabel='Covariates'>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1538,7 +1537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1547,7 +1546,7 @@
        "<Axes: title={'center': 'Propensity Distribution'}, xlabel='Propensity', ylabel='Probability density'>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1589,7 +1588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1598,7 +1597,7 @@
        "<Axes: title={'center': 'Propensity Distribution'}, xlabel='Propensity', ylabel='Probability density'>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1633,7 +1632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1655,7 +1654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1664,7 +1663,7 @@
        "Text(0.5, 1.0, 'IPW weights (log y)')"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1707,205 +1706,271 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## AIPW & DR에 대한 Robustness Check 흐름"
+    "[참고자료]\n",
+    "- Yang, Jeff Y., et al. \"Propensity score methods to control for confounding in observational cohort studies: a statistical primer and application to endoscopy research.\" Gastrointestinal endoscopy 90.3 (2019): 360-369.\n",
+    "\n",
+    "- Austin, Peter C. \"Informing power and sample size calculations when using inverse probability of treatment weighting using the propensity score.\" Statistics in Medicine 40.27 (2021): 6150-6163.\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "AIPW에는 IPW뿐만 아니라 Outcome에 대한 Term이 포함되기 때문에    \n",
-    "**위에서 진행한 IPW에 대한 Robustness Check뿐만 아니라 Outcome에 대한 Robustness Check 및 AIPW 자체에 대한 검사도 함께 진행해야 합니다.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from causallib.estimation.ipw import IPW\n",
-    "from causallib.estimation.doubly_robust import AIPW\n",
-    "from causallib.estimation.standardization import Standardization\n",
-    "from sklearn.linear_model import LogisticRegression, Ridge\n",
-    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
-    "from causallib.estimation import TMLE"
+    "## Double/Debiased Machine Learning (비모수 버전의 Regression 처럼 활용 가능)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "출처: https://causallib.readthedocs.io/en/latest/causallib.estimation.doubly_robust.html?utm_source=chatgpt.com"
+    "코드 및 데이터 참조 출처: https://matheusfacure.github.io/python-causality-handbook/22-Debiased-Orthogonal-Machine-Learning.html"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- 추론 자체의 견고성 확인    \n",
-    ": 부트스트랩 신뢰구간을 우선적으로 구하여 DR ATE 추정치의 표본 불확실성을 먼저 평가해봅시다."
+    "DML은 ATE와 CATE와 같은 인과적 모수를 구하는 하나의 프레임입니다.   \n",
+    "\n",
+    "간략하게 소개하자면, DML은 복잡한 보조모형은 머신러닝으로 학습하고, 직교화(Neyman 점수)와 교차적합으로 bias를 상쇄해 ATE·CATE 같은 인과모수를 정규성으로 안정적으로 추정하는 프레임워크입니다.             \n",
+    "이를 통해 인과 매개변수의 추정 절차와 성가신 매개변수의 추정 절차를 분리할 수 있는 장점을 지닌 방법입니다.\n",
+    "\n",
+    "코드와 함께 더 자세하게 알아보기 위해 계량경제학에서 자주 사용하는 데이터셋 중 하나인 **아이스크림 판매 데이터셋**을 활용해보겠습니다.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AIPW ATE 95% bootstrap CI: [0.4139, 0.4879]\n"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temp</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>cost</th>\n",
+       "      <th>price</th>\n",
+       "      <th>sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>25.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>23.3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>26.9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>227</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   temp  weekday  cost  price  sales\n",
+       "0  17.3        6   1.5    5.6    173\n",
+       "1  25.4        3   0.3    4.9    196\n",
+       "2  23.3        5   1.5    7.6    207\n",
+       "3  26.9        1   0.3    5.3    241\n",
+       "4  20.2        1   1.0    7.2    227"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "def _aipw_ate_once(Xb, Tb, Yb):\n",
-    "    om = Standardization(learner=Ridge(alpha=1.0))\n",
-    "    wm = IPW(learner=LogisticRegression(max_iter=1000),\n",
-    "             clip_min=0.01, clip_max=0.99, use_stabilized=True)\n",
-    "    dr_b = AIPW(outcome_model=om, weight_model=wm)\n",
-    "    dr_b.fit(Xb, Tb, Yb)\n",
-    "    mu_b = dr_b.estimate_population_outcome(Xb, Tb, Yb)\n",
-    "    return float(dr_b.estimate_effect(mu_b[1], mu_b[0], agg=\"population\")[\"diff\"])\n",
-    "\n",
-    "B = 500\n",
-    "rng = np.random.default_rng(42)\n",
-    "n = len(Y)\n",
-    "ates = []\n",
-    "for b in range(B):\n",
-    "    idx = rng.integers(0, n, n)     # 부트스트랩 인덱스\n",
-    "    ates.append(_aipw_ate_once(X.iloc[idx], T.iloc[idx], Y.iloc[idx]))\n",
-    "\n",
-    "lo, hi = np.percentile(ates, [2.5, 97.5])\n",
-    "print(f\"AIPW ATE 95% bootstrap CI: [{lo:.4f}, {hi:.4f}]\")"
+    "test = pd.read_csv(\"../data/matheus_data/ice_cream_sales_rnd.csv\")\n",
+    "train = pd.read_csv(\"../data/matheus_data/ice_cream_sales.csv\")\n",
+    "train.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "부트스트랩으로 구한 ATE의 CI 구간이 0을 포함하지 않고, 그 구간의 범위도 넓지 않아 신빙성 있게 ATE를 구한 것을 확인할 수 있습니다.     \n",
-    "\n",
-    "다음으로 DR모델 자체의 강건성 검증을 해보도록 하겠습니다."
+    "### Frisch-Waugh-Lovell 응용 DML"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 35,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "- DR 스트레스 테스트 — 결과모형 및 PS모형 각각 하나씩 변경해보기 "
+    "y = \"sales\"\n",
+    "T = \"price\"\n",
+    "X = [\"temp\", \"weekday\", \"cost\"]\n",
+    "\n",
+    "debias_m = LGBMRegressor(max_depth=3, verbosity=-1)\n",
+    "denoise_m = LGBMRegressor(max_depth=3, verbosity=-1)\n",
+    "\n",
+    "train_pred = train.assign(price_res =  train[T] - cross_val_predict(debias_m, train[X], train[T], cv=5),\n",
+    "                          sales_res =  train[y] - cross_val_predict(denoise_m, train[X], train[y], cv=5))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "위에서 말했듯 DR에서는 결과모형, PS모형 모두 사용하고 둘 중 하나만 맞으면 결과값이 안정적이라는 장점이 있습니다.     \n",
+    "위의 코드는 FWL(Frisch-Waugh-Lovell) 정리($Y_i - \\mathbb{E}[Y_i\\mid X_i] = \\tau\\,(T_i - \\mathbb{E}[T_i\\mid X_i]) + \\varepsilon$)에서 $ \\mathbb{E}[Y_i\\mid X_i] $ 와 $\\mathbb{E}[T_i\\mid X_i] $를 머신러닝을 사용하여 추정합니다.    \n",
+    "-> $Y_i - \\mathbb{E}[Y_i\\mid X_i] = \\tau\\,(T_i - \\mathbb{E}[T_i\\mid X_i]) + \\varepsilon$)에서 $\n",
     "\n",
+    "이를 통해 Y와 T의 잔차를 추정할 때 교호작용(변수 간의 Interaction)과 비선형성을 모델링할 수 있고, 동시에 FWL 스타일의 직교화를 유지할 수 있게끔 합니다.     \n",
     "\n",
-    "이러한 DR의 특성을 사용해서 각각의 모형을 하나씩만 바꿨을 때 ATE가 크게 변동이 없으면 모델의 안정성이 확보되는 것으로 해석할 수 있습니다. (Knaus et al. (2021))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base_om = Standardization(learner=Ridge(alpha=1.0))\n",
-    "base_wm = IPW(learner=LogisticRegression(max_iter=1000),\n",
-    "              clip_min=0.01, clip_max=0.99, use_stabilized=True)\n",
-    "dr_base = AIPW(outcome_model=base_om, weight_model=base_wm).fit(X, T, Y)\n",
-    "mu_base = dr_base.estimate_population_outcome(X, T, Y)\n",
-    "ate_base = float(dr_base.estimate_effect(mu_base[1], mu_base[0], agg=\"population\")[\"diff\"])"
+    "변수명을 debias_m, 그리고 denoise_m이라고 지정한 이유는 무엇일까요?    \n",
+    "\n",
+    "먼저 debias_m은 FWL 정리의 식에서 $T - M_t( = \\tilde{T})$부분으로, X의 모든 교란 편향이 모델에 의해 제거된 부분입니다.($M_t := \\mathbb{E}[T_i\\mid X_i]$를 머신러닝으로 추정한 모델)        \n",
+    "즉, $\\tilde{T}$는 X에 직교하는, X로 인한 bias를 없앤 값입니다.    \n",
+    "\n",
+    "마찬가지로 denoise_m은 FWL 식에서 $Y - M_y(= \\tilde{Y})$부분으로, Y에서 분산을 제거하는 부분입니다.($M_y := \\mathbb{E}[Y_i\\mid X_i]$를 머신러닝으로 추정한 모델)    \n",
+    "즉, $\\tilde{Y}$는 X로 인한 모든 분산이 제거된 값입니다."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* 1) 결과 모형만 교체: 비선형 예측기(Random Forest)로 변경"
+    "위에서 구한 모델을 활용하여 최종 ATE를 구해봅시다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>    0.0106</td> <td>    0.072</td> <td>    0.148</td> <td> 0.883</td> <td>   -0.131</td> <td>    0.152</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>price_res</th> <td>   -3.9228</td> <td>    0.071</td> <td>  -54.962</td> <td> 0.000</td> <td>   -4.063</td> <td>   -3.783</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "\\toprule\n",
+       "                    & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{Intercept}  &       0.0106  &        0.072     &     0.148  &         0.883        &       -0.131    &        0.152     \\\\\n",
+       "\\textbf{price\\_res} &      -3.9228  &        0.071     &   -54.962  &         0.000        &       -4.063    &       -3.783     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\end{center}"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.table.SimpleTable'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "altY_om = Standardization(learner=RandomForestRegressor(n_estimators=400, min_samples_leaf=5, random_state=0))\n",
-    "dr_altY = AIPW(outcome_model=altY_om, weight_model=base_wm).fit(X, T, Y)\n",
-    "mu_altY = dr_altY.estimate_population_outcome(X, T, Y)\n",
+    "import statsmodels.formula.api as smf\n",
     "\n",
-    "\n",
-    "ate_altY = float(dr_altY.estimate_effect(mu_altY[1], mu_altY[0], agg=\"population\")[\"diff\"])"
+    "final_model = smf.ols(formula='sales_res ~ price_res', data=train_pred).fit()\n",
+    "final_model.summary().tables[1]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* 2) PS모형만 교체: 비선형 분류기 (RF)로 변경"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "altPS_wm = IPW(learner=RandomForestClassifier(n_estimators=400, min_samples_leaf=5, random_state=0),\n",
-    "               clip_min=0.01, clip_max=0.99, use_stabilized=True)\n",
-    "dr_altPS = AIPW(outcome_model=base_om, weight_model=altPS_wm).fit(X, T, Y)\n",
-    "mu_altPS = dr_altPS.estimate_population_outcome(X, T, Y)\n",
-    "\n",
+    "위와 같이 최종 ATE 추정 시에는 간단하게 선형으로 ATE를 추정할 수 있습니다.     \n",
+    "    \n",
+    "하지만 이는 결국 True Y값이 비선형적인 모습을 띄고 있다면 ATE를 제대로 추정하지 못하게 되는 단점을 가지고 있습니다.        \n",
     "\n",
-    "ate_altPS = float(dr_altPS.estimate_effect(mu_altPS[1], mu_altPS[0], agg=\"population\")[\"diff\"])"
+    "그래서 우리는 비모수 이중/탈편향 기계학습 방법을 통해 최종 모델까지도 비선형 ML을 사용하는 방법을 알아볼 것입니다."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Y와 Propensity Score를 구할 때 위에서는 Ridge로 구했었지만, 이번에는 학습기의 종류를 바꾸어 학습기의 교체로 인해 ATE가 크게 흔들리는지 확인해봅시다.   "
+    "#### 비모수 이중/탈편향 기계학습"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'AIPW_ATE_base': 0.45041234661005924, 'AIPW_ATE_altY_only': 0.4393682311294207, 'AIPW_ATE_altPS_only': 0.4481943633726966}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print({\n",
-    "    \"AIPW_ATE_base\": ate_base,\n",
-    "    \"AIPW_ATE_altY_only\": ate_altY,\n",
-    "    \"AIPW_ATE_altPS_only\": ate_altPS\n",
-    "})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": null,
    "metadata": {},
-   "source": [
-    "예측기의 종류에 따라 ATE의 결과가 변동성이 거의 없는 것을 확인할 수 있습니다.   \n",
-    "-> DR 모형의 안정성 확보"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- Double Machine Learning (비모수 버전의 Regression 처럼 활용 가능)"
+    "### 질문이나 의견을 남겨주세요.\n",
+    "<script src=\"https://utteranc.es/client.js\"\n",
+    "        repo=\"CausalInferenceLab/awesome-causal-inference-python\"\n",
+    "        issue-term=\"pathname\"\n",
+    "        theme=\"github-light\"\n",
+    "        crossorigin=\"anonymous\"\n",
+    "        async>\n",
+    "</script>"
    ]
   }
  ],

From b59743435ab54b453851c5e0cf973ee5a0164a98 Mon Sep 17 00:00:00 2001
From: MinSeok Kang <rkdals1148@gmail.com>
Date: Tue, 28 Oct 2025 03:06:03 +0900
Subject: [PATCH 6/6] DML Update

---
 book/ate/propensity_score_and_dml.ipynb | 276 ++++++++++++++++++++++--
 1 file changed, 259 insertions(+), 17 deletions(-)

diff --git a/book/ate/propensity_score_and_dml.ipynb b/book/ate/propensity_score_and_dml.ipynb
index 13c97bc..c13582a 100644
--- a/book/ate/propensity_score_and_dml.ipynb
+++ b/book/ate/propensity_score_and_dml.ipynb
@@ -1734,14 +1734,25 @@
     "DML은 ATE와 CATE와 같은 인과적 모수를 구하는 하나의 프레임입니다.   \n",
     "\n",
     "간략하게 소개하자면, DML은 복잡한 보조모형은 머신러닝으로 학습하고, 직교화(Neyman 점수)와 교차적합으로 bias를 상쇄해 ATE·CATE 같은 인과모수를 정규성으로 안정적으로 추정하는 프레임워크입니다.             \n",
-    "이를 통해 인과 매개변수의 추정 절차와 성가신 매개변수의 추정 절차를 분리할 수 있는 장점을 지닌 방법입니다.\n",
+    "이를 통해 인과 매개변수의 추정 절차와 성가신 매개변수의 추정 절차를 분리할 수 있는 Frisch-Waugh-Lovell의 장점을 그대로 지닌 방법입니다.\n",
     "\n",
     "코드와 함께 더 자세하게 알아보기 위해 계량경제학에서 자주 사용하는 데이터셋 중 하나인 **아이스크림 판매 데이터셋**을 활용해보겠습니다.  \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lightgbm import LGBMRegressor\n",
+    "from sklearn.model_selection import cross_val_predict\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1826,7 +1837,7 @@
        "4  20.2        1   1.0    7.2    227"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1846,7 +1857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1866,17 +1877,20 @@
    "metadata": {},
    "source": [
     "위의 코드는 FWL(Frisch-Waugh-Lovell) 정리($Y_i - \\mathbb{E}[Y_i\\mid X_i] = \\tau\\,(T_i - \\mathbb{E}[T_i\\mid X_i]) + \\varepsilon$)에서 $ \\mathbb{E}[Y_i\\mid X_i] $ 와 $\\mathbb{E}[T_i\\mid X_i] $를 머신러닝을 사용하여 추정합니다.    \n",
-    "-> $Y_i - \\mathbb{E}[Y_i\\mid X_i] = \\tau\\,(T_i - \\mathbb{E}[T_i\\mid X_i]) + \\varepsilon$)에서 $\n",
     "\n",
     "이를 통해 Y와 T의 잔차를 추정할 때 교호작용(변수 간의 Interaction)과 비선형성을 모델링할 수 있고, 동시에 FWL 스타일의 직교화를 유지할 수 있게끔 합니다.     \n",
     "\n",
-    "변수명을 debias_m, 그리고 denoise_m이라고 지정한 이유는 무엇일까요?    \n",
+    "그렇다면 변수명을 debias_m, 그리고 denoise_m이라고 지정한 이유는 무엇일까요?    \n",
+    "\n",
+    "- **debias_m**  \n",
+    "  : FWL 정리의 식에서 $T - M_t( = \\tilde{T})$부분으로, X의 모든 교란 편향이 모델에 의해 제거된 부분입니다.($M_t := \\mathbb{E}[T_i\\mid X_i]$를 머신러닝으로 추정한 모델)          \n",
+    "  즉, $\\tilde{T}$는 X에 직교하는, X로 인한 bias를 없앤 값입니다.    \n",
     "\n",
-    "먼저 debias_m은 FWL 정리의 식에서 $T - M_t( = \\tilde{T})$부분으로, X의 모든 교란 편향이 모델에 의해 제거된 부분입니다.($M_t := \\mathbb{E}[T_i\\mid X_i]$를 머신러닝으로 추정한 모델)        \n",
-    "즉, $\\tilde{T}$는 X에 직교하는, X로 인한 bias를 없앤 값입니다.    \n",
+    "- **denoise_m**  \n",
+    "  :FWL 식에서 $Y - M_y(= \\tilde{Y})$부분으로, Y에서 분산을 제거하는 부분입니다.($M_y := \\mathbb{E}[Y_i\\mid X_i]$를 머신러닝으로 추정한 모델)            \n",
+    "  즉, $\\tilde{Y}$는 X로 인한 모든 분산이 제거된 값입니다.\n",
     "\n",
-    "마찬가지로 denoise_m은 FWL 식에서 $Y - M_y(= \\tilde{Y})$부분으로, Y에서 분산을 제거하는 부분입니다.($M_y := \\mathbb{E}[Y_i\\mid X_i]$를 머신러닝으로 추정한 모델)    \n",
-    "즉, $\\tilde{Y}$는 X로 인한 모든 분산이 제거된 값입니다."
+    "또한 debias_m과 denoise_m값을 추정할 때 K-Fold 방식을 사용하여 머신러닝의 고질적인 과적합 문제를 방지합니다."
    ]
   },
   {
@@ -1888,7 +1902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1922,7 +1936,7 @@
        "<class 'statsmodels.iolib.table.SimpleTable'>"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1934,30 +1948,258 @@
     "final_model.summary().tables[1]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>  192.9679</td> <td>    1.013</td> <td>  190.414</td> <td> 0.000</td> <td>  190.981</td> <td>  194.954</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>price</th>     <td>    1.2294</td> <td>    0.162</td> <td>    7.575</td> <td> 0.000</td> <td>    0.911</td> <td>    1.547</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/latex": [
+       "\\begin{center}\n",
+       "\\begin{tabular}{lcccccc}\n",
+       "\\toprule\n",
+       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
+       "\\midrule\n",
+       "\\textbf{Intercept} &     192.9679  &        1.013     &   190.414  &         0.000        &      190.981    &      194.954     \\\\\n",
+       "\\textbf{price}     &       1.2294  &        0.162     &     7.575  &         0.000        &        0.911    &        1.547     \\\\\n",
+       "\\bottomrule\n",
+       "\\end{tabular}\n",
+       "\\end{center}"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.table.SimpleTable'>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_model = smf.ols(formula='sales ~ price', data=train_pred).fit()\n",
+    "final_model.summary().tables[1]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "위와 같이 최종 ATE 추정 시에는 간단하게 선형으로 ATE를 추정할 수 있습니다.     \n",
     "    \n",
-    "하지만 이는 결국 True Y값이 비선형적인 모습을 띄고 있다면 ATE를 제대로 추정하지 못하게 되는 단점을 가지고 있습니다.        \n",
+    "그렇다면 DML을 활용해서 CATE를 구하는 방법은 무엇일까요?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DML 응용 CATE 계산"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CATE를 구하기 위해서는 ATE와 동일한 식을 사용하지만, 거기에 T잔차와 다른 공변량과의 상호작용($\\pmb{\\beta}_2 \\pmb{X_i} \\tilde{T_i}$)텀을 추가하도록 할 것입니다.        \n",
+    "\n",
+    "즉, $\\tilde{Y_i} = \\alpha + \\beta_1 \\tilde{T_i} + \\pmb{\\beta}_2 \\pmb{X_i} \\tilde{T_i} + \\epsilon_i$라는 식을 추정한다면 CATE 값을 얻을 수 있습니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_model_cate = smf.ols(formula='sales_res ~ price_res * (temp + C(weekday) + cost)', data=train_pred).fit()\n",
     "\n",
-    "그래서 우리는 비모수 이중/탈편향 기계학습 방법을 통해 최종 모델까지도 비선형 ML을 사용하는 방법을 알아볼 것입니다."
+    "cate_test = test.assign(cate=final_model_cate.predict(test.assign(price_res=1))\n",
+    "                        - final_model_cate.predict(test.assign(price_res=0)))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 비모수 이중/탈편향 기계학습"
+    "price_res * (temp + C(weekday) + cost)라는 T와 다른 공변량 간의 상호작용 텀을 넣어 CATE를 추정할 수 있도록 구성했습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def elast(data, y, t):\n",
+    "    return (np.sum((data[t] - data[t].mean())*(data[y] - data[y].mean())) /\n",
+    "            np.sum((data[t] - data[t].mean())**2))\n",
+    "\n",
+    "def cumulative_gain(dataset, prediction, y, t, min_periods=30, steps=100):\n",
+    "    size = dataset.shape[0]\n",
+    "    ordered_df = dataset.sort_values(prediction, ascending=False).reset_index(drop=True)\n",
+    "    n_rows = list(range(min_periods, size, size // steps)) + [size]\n",
+    "    return np.array([elast(ordered_df.head(rows), y, t) * (rows/size) for rows in n_rows])\n",
+    "\n",
+    "gain_curve_test = cumulative_gain(cate_test, \"cate\", y=y, t=T)\n",
+    "plt.plot(gain_curve_test, color=\"C0\", label=\"Test\")\n",
+    "plt.plot([0, 100], [0, elast(test, y, T)], linestyle=\"--\", color=\"black\", label=\"Baseline\")\n",
+    "plt.legend();\n",
+    "plt.title(\"R-Learner\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "위의 곡선에서 볼 수 있듯이 최종 선형 모델을 사용한 DML 절차는 훌륭한 성능을 나타냅니다.\n",
+    "\n",
+    "참고로, DML을 활용하여 CATE를 구하는 방법이 R-Learner라고 불리는 메타 학습자의 하나의 종류입니다."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Non Parametric Double/Debiased ML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "하지만 위에서 우리는 중간에 머신러닝을 사용하긴 했지만, 최종 ATE/CATE를 구할 때는 OLS, 즉 선형 모델을 사용한 것을 볼 수 있습니다.     \n",
+    "결국 우리는 아직 Y와 T간의 비선형성을 잡을 수 없는 한계가 있었습니다.    \n",
+    "\n",
+    "우리는 위에서 추정한 식의 변형을 통해 비선형성까지 머신러닝이 학습할 수 있게 코드를 짤 수 있습니다.    \n",
+    "식의 변형에 대해서는 다음의 링크를 참고해주세요. https://matheusfacure.github.io/python-causality-handbook/22-Debiased-Orthogonal-Machine-Learning.html "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "우리는 코드를 통해 Y와 T간의 비선형성을 잡을 수 있는 방법에 대해 알아보겠습니다.   \n",
+    "식의 변형은 다소 복잡할 수 있지만, 코드로 구현하면 간단합니다. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "model_final= LGBMRegressor(max_depth=3)\n",
+    "\n",
+    "# create the weights\n",
+    "w= train_pred[\"price_res\"]** 2\n",
+    "\n",
+    "# create the transformed target\n",
+    "y_star= (train_pred[\"sales_res\"]/ train_pred[\"price_res\"])\n",
+    "\n",
+    "# use a weighted regression ML model to predict the target with the weights.\n",
+    "model_final.fit(X=train[X], y=y_star, sample_weight=w);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "위의 기계학습 모델은 기존의 식을 변형해 가중치 w($\\tilde{T}_i^2$)와 y_star($\\frac{\\tilde{Y}_i}{\\tilde{T}_i}$) 텀을 추가해서    \n",
+    "X값을 Y값에 학습시키기만 해도 Y와 T의 비선형성을 포함하는 최종 CATE 값을 구할 수 있다는 장점이 있습니다."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "이제 테스트 데이터셋을 사용하여 이 비모수적 버전을 이전에 사용한 선형 버전과 비교해 보겠습니다.        \n",
+    "\n",
+    "먼저 개별 처치 효과를 추정합니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cate_test_non_param = test.assign(cate=model_final.predict(test[X]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "다음으로, 비모수 누적 탄성 곡선을 모수(선형) 버전의 이중/직교 기계학습에서 얻은 곡선과 나란히 플롯을 그릴 수 있습니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cI3q9SkRsMhGxyRw+n0TO1bP4cBVPJQUPJQVPkimnpOJlk46fQzbethm4KRnYK9lo8rIhNxNysyHn+r2DdK0MlRuBjT1obQwJh8YWHMqBi6/hcPaFjGsQtQqO/WHooimscgFQbyAEdwdFC3nZtw5HT3Cvkr+rJzMZTm2C43/B6U2G5Kh8NfAI4rqzP9HZHsRkOXE81YGoFFtOXc3FRqvg4WhDRcc8Ktjn4Gubgad6hfJ5V3DPvYxLTiJ5aEnVupOicSMJV/Q56QQl7aB66m50+kIkPooWPGuAb23wDUPv7o+akYSalgBplw2P1xPh+mU06YloMq+hUPCvPtXRgzydO9kaBzIUe9L09mRqncmxL0+OfXny7D3Isvci2qYGMRlOxCVncPZKOicuGZJDW63CYw39eL5dNbxcdOTkGWZ+5ej1uDnYorPRFvZfTohSS5IXSV4EhvEMUfEpbD+ZyNYTiew9c5XMHH3BFe+gHCm01ByllSaC1toIKin3kTDc5F4VKtY3HN6hcDkKTv0NZ3dC3n20IDiUNyQhbn6Qm3UjCcmB3AzISjO0smSlGRIn3zpQ/ynwa1q4Fp7ilpMJZ7bCifWG98TRw5BUOXoYEjk7R7B1AFtHQ6LnUqFw42rycg1jc3IzDe9Zbqah5epyNMTugwv7IOEo3KEL7K48qkPVFlC1JQfzApi5N5OtMam3FXPlOlWVBMrbZBLs60LtSu7UruxOVS93NBXrmWV8kBDWQJIXSV7KrDy9yv6z1/gzIo41R+JISMmfDHi56Air5EZ1H2eqe7tQ3dsZTxedce2TlIwcktJzuJiUTm58JJUvbSbs+g5C9CfQ/OsvdVVji+JWCZy8wMkbnG5+sLqDvfutR1tHw1gSG3tDl46Du+HD906y0+HcDkg8YUg+9LmGIy/b0LqSGm840hIABWp0htDeULWVoZVGFK3sdLhy0pD8ZV+/lQRmJhlacdIT4foVSDoLl6LgtpYchSynCpzK9eJkuiOVlET8lXg8lNsTmpuy0HHKpSGXfduhVn+IakHBtwZDC1HKSfIiyUuZEx2fyuLdZ/nzSDyXU28lLI52WpoFetAyyJPW1T2p7u1898GtmSlwdoehWyR6jeFD6d+8a0G19oaxKVWbg52TGb8jYVXSr8L53YbxR2d3GFpw7jGuSHX2JdvOjfSsXNKzc8nIzsONVLyU5HzlLqieXLbxBfeqlKsYRKWqQdja2BpahPR5hkdnH0NLmpOnub9LIcxKkhdJXsoEvV5ly4nLfLcthq0nEo3XXext6BzqS/cwX1pV97z7uILriXAx3NDaEfMPxB7I302g1RmSlZrdoHoXcJUtKoSJVNXw83UtBq7GGFrL3CqDRzUoH2iYPv4v2bl6jsUlc+XUfuxiNlDh0laqZkSipRDdnB5BUKUZVG0JtR4xdKsJYUUkeZHkpVTKydNz9sp1Tl5K40RCGqsOXeTkjYGRGgW61PLl8UZ+tAzyxM5GYxggm3TuRvP+jWb+1ASIP2yYIpx8hx3IywVAQBuo3tmQuEjrirCU9KtcvxjJyehI4s9Gk5UYg1vOZQDy0ICioZyzPdW0l3BN/c/Gtd6h0G8BeNWwQOBC3B9JXiR5KTVOX05j5cFY/jqawKnLaeTq8/+4OutseKKxH0Na+N8aG5ASB/u+MxzpiXd41X/xqA6VGhoSloDWhlk2QpRAqqpy9GIKa47E8WdEPDGJt2auedukM9gvga6uZwg8vxIl/TJ6G0dONp1GpGc3qnk5E1bZzYLRC1EwSV4kebFqV9Ky+O3QRVaEX+TQjeXyb3K009LQM5fmzpcIdc+jSc1KODq5GprIczJg/wI4uvzWmiS2juDsbZip4uRpePQONsz08a0D9vIzIqyPqqpEJ6Ty5+E4fj8cx+l/JTI+mmRmar+gpfYoAEty2zEldzDPdqzNKx2ro9GU4FlmokyT5EWSF6uj16vsjD7Hyc0/ERT3GxW5TDLOJOGCzsUDXy8PKuTFobt6DOX65YJf0K8ZNHsOgnvJTBxRqqmqSmRcCr8diuP3wxe5cC0DDXpes1vJ85pf0aByTu/FT3kduRjwCFMHdizU1hFCFBdJXiR5KfH0epW4lEzOXogl7thedFHLaJe7HWfFlD19FMPKr86+kJNuOLLTQZ9jmAnU7DlDy4oQZczNDTxd7W0Mu5Kf3gLLR9yYXg+5qoadNo0J6DyKyvUeMqy8LEQJIcmLJC+Wo6q3Fv3Kyzasj5GWACmxXI07w/ET0eivncM9O47KXMJVSc9X/YpdJfLq9Me7dnvD1OWMq4bVZDNTDONRfELBK1gG0gphquzrcHQF13d9h1PCfuNlPRoy3KphX7UR2koNoFoH8AyyYKCirJPkRZKX4qPPg/N74NjvhuPamUK/RLK2PFcrtadi2+HoAluU7JVfhbBiSWcj2LFsJvVSN1NRuZrvOb1ig6bTJGj+ImhkF21R/CR5keSlaOn1hkGwCUdB1RuPvPRraE6uR7l+6Y7VVBSysSNe70Y85YlTy2NXrjJVA2pQvnJ1PP1qYFve37DUuxCiWOTpVTYdu8SuQ0dJPLEb/+zjNNdE0lRzDIDcgA7YPDoXnL0sHKkoayR5keSl6JzdAWvHG9ZFuYtk1YktNGCLpinbs6qRprclCzty0AIKOhsNfRtUZngrf4K8Xe76OkKI4qXXq4RfSGL1wViy9nzPZJsF2Cs55Dh4YdtvHgS2s3SIogyR5EWSlwd37SysnwSRKwHI0DixWt+C1Fwb9GjQo5CDDbv1IezSh5DLrRk9rvY2BFdwJcTXheAKrnQO9cHDWWehb0QIYYpdp6/w2U+rmZz1MTU1F1BRUB/7Hk3tRywdmigjJHmR5OX+ZF837OIbuQr12B8oeVnkoWFJbntm5j7GFdwo52hLq+petK3hRYtqHjjYasnK1ZOdqycrNw8nnQ0V3Ozvvn+QEKLESkrP5q1le+hwcjp9tdu4oAvC9397sdHKGBhhfpK8SPJiOlU1bEJ4eIkhccm5NftnR14oU3MHccEukN71KvJow8rUreyOVha5EqLUUlWVn7dG0Gdje3RKLtOrfMPYwY9hKwmMMLPCfH7L6l1llarCyY3w9zSICzdePq9680deE/7Ma4pSqQFDm1ahZ52KOOnkR0WIskBRFJ5oU4f4453wvbAWn9O/8vyiQL4YUP/um5wKUczkE6ksOrsDNk4z7KYMZGsc+DG3AytymnNEDaBZoAcTO9eksX95CwcqhLAU3zbDYfFa+mi3Mz1yACN/1DP7qYbY20oCIyxPkpeyIv0qRPwC4YuMLS25ih0L9Q/xWXovruJKo6rlWNy5Bi2qeVo2ViGE5VXrAM6+lE+Lp6ttOL9F2zDih33MH9pEuo6FxUnyUtqd3gJ75xnGtehzAMhTbPhVbcfMjN7E40FYJTdmdq5B2xpeMtBWCGGgtYG6T8L2T5haNZyN55qy9UQi32+P4ZnWgZaOTpRxMmC3tMpKhb/egAM/GC+d0ASyKKsVq/NacBVXavg489pDNelSy0eSFiHE7RJPwBeNQNGyssMGXvkjDntbDX+90oaqHrJFhyhaMmC3jDp/NZ3tJxPJPL2D7ien4J0bh15V+CmvAwvzOhGlVkVRoI6fO5Na+NOrbkVp/hVC3J1ndajcBC7sobeylaWBTdl5+goTlkew6Jmm8kePsBhJXqyYqqpEJ6Ty15EE/joaz6m4RF60WcEo7Wq0isoF1ZOxOc9x2qk+bWp4MaqGF62DPCnnZGfp0IUQ1qLeALiwB+XQYqY/Ppyun21lx6kr/LzvPE80rmLp6EQZJcmLFcrO1bMyPJZ5W09zPCGNiiQy0GYDT+o24aGkAnC6Yk+utHmHzytVxNPZTv5CEkLcn9p9Ye0EuHwM/6xoxjxUk3f/jOKdP6JoV9MbH1d7S0coyiBJXqxIamYOS/ac59ttMSSmpNFME8UYu408pNmHBr2hkJsfdJ5GYK1HkCF1QogHZu8GIb0g4mcIX8jQbjP4/fBFDl1IZtKqI8x5upGlIxRlkAzYtQKnLqfx0+5zrN53knrZB+ii3Ucn7UHcSLtVyL81NB0JNboZZgkIIURROb0ZfugNOjcYe5yoxGx6fb6NXL3K1wMb0C2sgqUjFKWADNgtBbJz9ayPTGDZzmgczv5ND+0uXtOE42iXdauQoweE9obGI8An1HLBCiFKN/824FIBUuPg7DZCgjoxql01Pv/7JO/8EUX7YG9ZvE4UK0leSqAt0ZdY/csC2mVu4CvNwXwJi+pWGSW4F4T0BL9m0soihDA/jQZqdIH98yF6LQR14vl2QSzbd4HYpAy+336GUe2qWTpKUYbITlslSHJGDh8s/B1lYV9m5LxDL+0uHJUscl39oOXLMGITyitHoNv74N9KEhchRPGp0dXwePwvUFUc7LT8r2tNAL7cdJLEtKx7VBaiaMmnXwmx+fBpzq6cyqt5q7HT5pGr2KI2GoZtvSewqdgAZLaQEMKSAtqCjT0kn4NLkeBTiz71KvH99jNExCYza/1x3n0kzNJRijJCWl4s7OCxEyz+dBw1fu3IYP0K7JQ8kiq3x2b0bmx7fAiVGkriIoSwPDtHQwIDcHwtABqNwls9QgD4ac85jiekWio6UcZI8mIBam42R/7+iT3Tu1H7p6YMuDabispVrukqktVvMe7DV4CH9B8LIUqYmje6jqLXGi81DfSgay1f9Cq8+0eUhQITZY10GxUTVVWJPnmCa/98Q40Lv1BbvWp4QoHzDiE4NhmER6uhYOtg2UCFEOJuqncxPF7YC9cTwcmwA/34bsFsPJbAluOX2Rx9iXY1vS0YpCgLJHkxszOX09i4/nf8Ti6kfd4ObJU8AK6orhz37UFQ52fxq9bAwlEKIYQJ3CqBbx2IPwwn1hm2DgD8PZ0Y3NyfedtieOePKBpWLYeLva2FgxWlmXQbmUlmTh6Llq8k7vPODD8+ks76rdgqeZzU1WJX/Q/Rjo2i+ajZeEniIoSwJsZZR2vzXX6xQ3XKO9lx8lIaT83bzbXr2RYITpQVssKuGezcv5/rf06mU95WAHKw5ZL/w3h0GI19FUlWhBBWLHY/fNMB7Fzgf6fB5tZGrxEXkhn03W6upedQ08eFH59pgreL7H0kTFOYz29peSlC8QlxbPjkGRqs7kynvK3oUYit0hubl/dTach3krgIIaxfhfrg7APZqXB2e76nwiq78fPI5ni76IhOSOXx2Tu5cC3dQoGK0kySlyKQm5PN9sXT0X3VmE5Jy9ApuZx2bUzm0L+pNOwHlHJVLR2iEEIUDY0Gqnc2nP+n6wiguo8LvzzXgsrlHDhzJZ3HZ+/k7JXrxRykKO0keXlAJ7avIHZ6A1oef59ySirntVU41+0HAl9dj2NVaWkRQpRCN8e9RK+BO4w8qOLhyLLnmlPNy4mLyZm8ueJIMQcoSjtJXu7TtbgzHJ3Zk+rrh1BVf55ruLCv9ltUmnCAKk17y8JyQojSK7AdaHWQdBYuR9+xSAU3B+YPbYJWo7DtZCIHz10r3hhFqSbJSyHl5eWx6+ePsJnTjFopW8lRtWzxeBxePEijx15HYyPTA4UQpZzOGQJaG86j/7xrMb/yjjxSvxJg2P9IiKJSLMnLl19+ib+/P/b29jRt2pQ9e/bcs/yyZcsIDg7G3t6esLAw/vzz7v85itORw/uInN6GZpHv4EIG0doaHO/zO21f/IZyHl6WDk8IIYpPSC/D44EfQJ9312LPt6uGosCGqEscvZhcTMGJ0s7sycvSpUt57bXXmDx5MgcOHKBu3bp06dKFS5cu3bH8jh076N+/P8OHD+fgwYP06dOHPn36cOSIZftMD/z9C9V/7UpY7hEy0LEn+H8ETdhJrfotLBqXEEJYRFg/sHeDazFwYv1diwV6OdOzTkUAvtp0qriiE6Wc2dd5adq0KY0bN+aLL74AQK/X4+fnx4svvsj48eNvK//EE09w/fp1fv/9d+O1Zs2aUa9ePWbPnl3g/cy1zktW2jVSZjTkqlM1vPt/RblK1YvstYUQwiqtewt2fG4YAzNo1V2LHYtPoesnW1EUWP9qG4K8XYovRmE1Ssw6L9nZ2ezfv59OnTrduqFGQ6dOndi5c+cd6+zcuTNfeYAuXbrctXxWVhYpKSn5DnPQOZfDfuR6puz35PDJi2a5hxBCWJUmz4KigdOb4dLdN2UM9nWlc6gPqiqtL6JomDV5SUxMJC8vDx8fn3zXfXx8iI+Pv2Od+Pj4QpWfPn06bm5uxsPPz69ogr+DH35dy5IlS2jXrh3PPPMMV69eNdu9hBCixHOvAsE9DOe7790yPrpDEACrDl2UdV/EA7P62UYTJkwgOTnZeJw/f95s9xo4cCAjR44E4NtvvyUkJISlS5dSynZYEEII0zUdZXg8tBTS7/4HXZ3K7rSt4UWeXmX2Fml9EQ/GrMmLp6cnWq2WhISEfNcTEhLw9fW9Yx1fX99CldfpdLi6uuY7zMXd3Z3Zs2ezdetWQkJCuHTpEk8++SQ9e/bk7NmzZruvEEKUWFVbgG8Y5GbAgQX3LPrijdaXX/Zf4PTltOKITpRSZk1e7OzsaNiwIRs3bjRe0+v1bNy4kebNm9+xTvPmzfOVB1i/fv1dy1tCq1atOHjwIG+//TZ2dnb8+eefPPPMM5YOSwghip+i3Gp92TMP8nLvWrSRf3na1fQiJ09l4qoj0mot7pvZu41ee+01vvnmGxYsWEBUVBSjRo3i+vXrDB06FIBBgwYxYcIEY/mXX36ZtWvXMmPGDI4dO8aUKVPYt28fo0ePNneohaLT6Zg0aRKHDh2iY8eOzJo1y9IhCSGEZdR+FBw9IeUCHPvtnkWnPlwbnY2G7SevsCpcJj+I+2P25OWJJ57g448/ZtKkSdSrV4/w8HDWrl1rHJR77tw54uLijOVbtGjB4sWLmTt3LnXr1uWXX35h5cqV1K5d29yh3pfg4GA2bNiQL7433niD119/nevXZVCaEKIMsLWHRoY/SNl174G7VTwceamjYamJd/6IJCk929zRiVLI7Ou8FDdzrfNiqpiYGKpXr05eXh7+/v7Mnj2bLl26FHscQghRrFLi4JPaoM+F57YZxsHcRXaunh6fbeXEpTT6N/Fjet86xRioKKlKzDovZVFAQACrVq2iSpUqnDlzhq5duzJw4MC7rigshBClgmuFW9OmD/xwz6J2NhrefcSQ3Py05zz7zsiyE6JwJHkxgx49enD06FFeeeUVNBoNixcvJjg4mO+//14GqAkhSq8Ggw2Ph5ZCdvo9izYJKM/jjSoD8OaKI+Tk6c0dnShFJHkxE2dnZ2bNmsWuXbuoW7cu165d4+WXX75tGrgQQpQage0NC9dlJUPk3bcLuGlCtxDKO9kRnZDKgh1nzB+fKDUkeTGzxo0bs3fvXj788ENmzJiRb72avLy778QqhBBWR6OB+oMM5wWs+QJQzsmOsZ1rArBw11lpmRYmk+SlGNja2vL6668zYsQI47V169ZRr149du3aZcHIhBCiiNUfaNjv6NxOuBxdYPHe9SriaKflzJV0Dpy7VgwBitJAkhcLUFWVSZMmceTIEVq0aMHo0aPNtqGkEEIUK9eKUP3GDMsCBu4COOls6Frb0CL964FYc0YmShFJXixAURT++OMPhgwZgqqqfPnll4SGhrJy5UpLhyaEEA+u4RDDY/hiyM0qsPhjDQwDd38/dJHMHOlOFwWT5MVCPDw8+P7779mwYQPVqlUjNjaWRx55hL59+xIbK399CCGsWFAncKkIGVfh2O8FFm8W6EFFN3tSMnP5+5gsKyEKJsmLhXXs2JGIiAgmTJiAjY0NK1askHEwQgjrprWB+k8ZzvcXPHBXo1HoU78SAL/uv2DOyEQpIclLCeDg4MB7773H/v37eeONN+jbt6/xufT0e6+VIIQQJVKDpwEFYrbA1dMFFu97o+to8/HLJKYV3NUkyjZJXkqQOnXq8O6776IoCgCXL18mKCiIiRMnkpmZaeHohBCiENyrQLUOhnMTBu4GeTtT18+dPL3KatmwURRAkpcSbPHixcTFxfHOO+9Qp04dNm/ebOmQhBDCdDcH7h5cBLkFb8D4aIMbXUcHpOtI3JskLyXYSy+9xK+//kqFChU4ceIE7du3Z/jw4Vy9KvuACCGsQM1u4OwD1y9B9B8FFu9ZpyK2WoWjF1M4Fi/LR4i7k+SlBFMUhb59+xIVFcWoUaMA+O677wgJCWHJkiUWjk4IIQqgtYX6TxvO931fYPHyTna0r+kNwApZ80XcgyQvVsDNzY2vvvqKbdu2ERoayqVLl6QLSQhhHRoOxjhw98qpAos/2tAwcHfFwVhyZbNGcReSvFiRli1bcvDgQT788EPef/994/UrV66Qm5trwciEEOIu3KtA9YcM5/sLbn1pX9Mbd0dbLqVmsfP0FTMHJ6yVJC9Wxs7Ojtdffx13d3fAsNXAk08+SdOmTTlw4IBlgxNCiDtpNMzwaMKKu3Y2GnqEVQBglcw6EnchyYuVi4mJYf/+/Rw4cIDGjRszduxYrl+/bumwhBDilqCHwLUSpF+BqN8KLH5zwbq1R+JluwBxR5K8WLnAwECioqLo378/er2eGTNmUKtWLdasWWPp0IQQwkBrAw0GGc73fVdg8YZVylHJ3YG0rFw2Rsl2AeJ2kryUAj4+PixevJg///yTqlWrcvbsWbp3786AAQNISkqydHhCCGFIXhQtnN0Ol6PvWVSjUXi4XkUAVoXLrCNxO0leSpFu3bpx5MgRXnvtNTQaDeHh4Tg4OFg6LCGEANeKUKOr4Xz//AKL976RvGyOvkxyeo4ZAxPWSJKXUsbZ2ZkZM2awZ88eFixYgE6nAyAnJ4dTpwqepiiEEGZjHLi7CHIy7lk02NeVYF8XsvP0rDkSVwzBCWsiyUsp1bBhQxo3bmz8etasWdSqVYt3332X7OyCl+kWQogiV60DuFeFzGSI+KXA4je7jlZK15H4D0leygBVVdm1axdZWVm89dZbNGjQgJ07d1o6LCFEWaPRQONnDOd75oCq3rP4w3UNycvumKvEJd+7pUaULZK8lAGKovDrr7+yaNEivLy8OHr0KC1btuSFF14gOTnZ0uEJIcqS+k+BjQPER8C5XfcsWrmcI038y6Oq8NshWfNF3CLJSxmhKAoDBgwgKiqKoUOHoqoqX331FaGhobLVgBCi+DiWhzr9DOd75hRY/NasI0lexC2SvJQxHh4efPfdd2zcuJGgoCASExPx8fGxdFhCiLKkybOGx6jfIOXeSUmPsArYaAw7TZ+8lFoMwQlrIMlLGdWhQwcOHz7MmjVrCAkJMV7ftm0ber1shiaEMCPfMKjSAvS5Be42Xc7JjrY1vADDZo1CgCQvZZqDgwMdOnQwfr1v3z7atm1Lq1atOHLkiAUjE0KUek1vtL7s/77A/Y76NjDsNL1073mycmW7ACHJi/iXmJgYnJyc2LlzJ/Xr1+ett94iMzPT0mEJIUqj4J7gUhGuX4bIVfcs2rmWDxXc7ElMy+a3Q7Lmi5DkRfxLv379iIyMpHfv3uTm5vLuu+9Sp04dNm3aZOnQhBCljdb21qJ1u+89cNdWq+Hp5lUB+H57DGoBU6xF6SfJi8incuXKrFy5kuXLl1OxYkVOnDhBhw4deOWVVywdmhCitGk4BLR2ELsPYvffs2j/xlWwt9Vw9GIKe89cK574RIklyYu4o0ceeYTIyEief/55FEXJN6hXCCGKhLMX1OprOC9gt+lyTnY8Ur8SYGh9EWWbJC/irtzc3Pjyyy/Zv38/I0aMMF7ftWsXMTHyy0MIUQQaDDI8Rv5W4MDdIS0CAPjraDwXrqWbOzJRgknyIgpUv359NBrDj0paWhpPPPEEtWrV4uOPPyY3N9fC0QkhrFqV5uBSAbKS4eTGexat6etCyyAP9Cr8sPNsMQUoSiJJXkShJCcnExgYSEZGBq+//jpNmjRh//5791ULIcRdaTS3uo6O/Fpg8WEtDa0vS/acIz1b/ngqqyR5EYVSqVIl/v77b7799lvKlSvHwYMHadKkCa+99hppaWmWDk8IYY3CHjU8Rv8J2dfvWbR9TW/8PRxJyczl1wOyaF1ZJcmLKDRFURg2bBhRUVH0798fvV7PrFmzqFWrFomJiZYOTwhhbSo2gHL+kJMOx9fes6hGozC4hT8A87fHoNfLtOmySJIXcd98fHxYvHgxf/75J1WrVqVx48Z4enpaOiwhhLVRFKh9o/XlyPICi/dr5IezzoZTl6+z9aT8wVQWSfIiHli3bt04evQoc+bcWmgqISGB7777ThaTEkKY5mbycmIdZCTds6izzobHGhq2DFiw44x54xIlkiQvokg4OTnh4eFh/PqVV15h+PDhtG/fnujoaAtGJoSwCj61wCsE8rLh2B8FFr/ZdbQp+hJnEu89TkaUPpK8iCKnqiqNGzfG0dGRLVu2UKdOHaZNm0Z2dralQxNClGTGrqOCZx0FeDrRrqYXqkybLpMkeRFFTlEUXnvtNY4cOULXrl3Jzs5m0qRJ1K9fnx07dlg6PCFESVX7xpTp05vhesFjWW62vizbd57rWTJtuiyR5EWYTUBAAH/++SeLFy/Gy8uLyMhIWrZsyc8//2zp0IQQJZFHNahYH9Q8iFxZYPG21b0I8HQiNSuX5Qdl2nRZIsmLMCtFUejfvz/Hjh1j2LBhVK5cma5du1o6LCFESVWIWUcajcKgG7tNL9hxRiYIlCFmTV6uXr3KwIEDcXV1xd3dneHDhxe4kFm7du1QFCXf8dxzz5kzTFEMypcvz7fffktERASurq6AYWzMpEmTOH/+vIWjE0KUGLUeMTye3Q5JBf9ueKxhZZzstJy8lMb2k1fMHJwoKcyavAwcOJCjR4+yfv16fv/9d/755x+effbZAuuNGDGCuLg44/Hhhx+aM0xRjNzd3Y3n8+fPZ9q0aYSGhvLFF1+Ql5dnucCEECWDW2Xwb204D19cYHEXe1vjtOn5Mm26zDBb8hIVFcXatWuZN28eTZs2pVWrVnz++ecsWbKEixcv3rOuo6Mjvr6+xuPmX+qidGnSpAnNmzcnLS2NF198kZYtWxIREWHpsIQQllb/acNj+ELQ6wssPujGwN2NxxI4f1V2my4LzJa87Ny5E3d3dxo1amS81qlTJzQaDbt3775n3UWLFuHp6Unt2rWZMGEC6el3/2HMysoiJSUl3yGsQ61atdi2bRtfffUVLi4u7N69mwYNGvDGG2+QkZFh6fCEEJYS+jDo3CDpHMRsKbB4NS9nWlf3vDFt+oz54xMWZ7bkJT4+Hm9v73zXbGxsKF++PPHx8XetN2DAABYuXMimTZuYMGECP/74I0899dRdy0+fPh03Nzfj4efnV2TfgzA/jUbDqFGjiIqKom/fvuTm5jJ9+nT69+9v6dCEEJZi6wBhjxnOD/5oUpWhLf0BWLJXpk2XBYVOXsaPH3/bgNr/HseOHbvvgJ599lm6dOlCWFgYAwcO5IcffmDFihWcOnXqjuUnTJhAcnKy8ZDBn9apUqVK/Prrr6xYsQI/Pz/GjRtn6ZCEEJbU4EbXUdTvkH61wOLtangbpk1n5vLrgQtmDk5Ymk1hK4wZM4YhQ4bcs0xgYCC+vr5cunQp3/Xc3FyuXr2Kr6+vyfdr2rQpACdPnqRatWq3Pa/T6dDpdCa/nijZ+vTpQ/fu3bGzszNe++KLLyhXrhwDBgxAURQLRieEKDYV6oFPGCREQMQyaDrynsU1GoWhLf2ZtOoo328/w1NNq6LRyO+L0qrQLS9eXl4EBwff87Czs6N58+YkJSWxf/9+Y92///4bvV5vTEhMER4eDkCFChUKG6qwUv9OXE6dOsXYsWN56qmn6Nq1K6dPn7ZgZEKIYqMot1pfDvwIJqzh8miDyrja2xCTeJ1N0ZcKLC+sl9nGvISEhNC1a1dGjBjBnj172L59O6NHj+bJJ5+kYsWKAMTGxhIcHMyePXsAwwfVtGnT2L9/P2fOnGH16tUMGjSINm3aUKdOHXOFKkowPz8/Jk2ahE6nY926ddSuXZuPPvqI3Fzp0xai1AvrB1o7Q+tLXHiBxZ10NvRvUgWA77bHmDk4YUlmXedl0aJFBAcH07FjR7p3706rVq2YO3eu8fmcnByio6ONs4ns7OzYsGEDnTt3Jjg4mDFjxvDoo4/y22+/mTNMUYLZ2dnxxhtvEBERQfv27cnIyOB///sfjRs3Zt++fZYOTwhhTo7lIbin4fyAaQN3B7XwR6tR2H7yCsfiZfZpaaWopWw95ZSUFNzc3EhOTpb1YUoZVVWZP38+Y8aM4dq1azg6OnL+/HnKly9v6dCEEOZy6m/48RHD1Omx0YaZSAV4YdEB/oiI44lGfnzwmLTaW4vCfH7L3kbCaiiKwtChQzl27BgDBgzgjTfekMRFiNIuoB24VYGsZIgyrRV+WCt/AFaEx5KYlmW20ITlSPIirI63tzeLFi3ijTfeMF7btWsX/fv3v+caQkIIK6TRQP2BhvP9802q0qBKOepWdiM7V8/i3efMF5uwGElehNW6OW1aVVWef/55lixZQkhICPPmzUNvwpLiQggrUf9pULSGzRovRRVYXFEUhrUKAOCHnWfJypV900obSV6E1VMUhW+//ZaGDRuSlJTEiBEjaN++/QMtliiEKEHcKkHNbobzfd+ZVKVb7Qr4uOpITMvil/2yaF1pI8mLKBXq16/Prl27mDlzJo6Ojvzzzz/UrVuXqVOnkpUlfd5CWL1GwwyPh5ZAVlqBxe1sNDzX1rCw6VebTpGdK62xpYkkL6LUsLGx4dVXX+Xo0aN069aN7OxsJk+ezPLlyy0dmhDiQQW2h3IBkJUCR34xqUr/JlXwctERm5QhrS+ljCQvotTx9/fnjz/+YMmSJTz++OM8+eSTxudK2coAQpQdGs2t1pe935q04q69rdbY+vLlppPS+lKKSPIiSiVFUXjiiSdYunSpcWBvSkoKzZo145dffpEkRghrVP8p0Oog/jDE7i+4PDCwaRU8nQ2tL8tlw8ZSQ5IXUWbMmjWLPXv20K9fP/r06SM7kAthbRzLQ61HDOd7vzWpiqH1JRCALzadJCdPWl9KA0leRJkxbtw4Jk6ciK2tLatXryY0NJTPP/+cvDyZRimE1Wg83PB4dDmkXzWpysCmVfF0tuPCtQxWHIg1Y3CiuEjyIsoMe3t7pk6dysGDB2nRogVpaWm89NJLtGjRgsOHD1s6PCGEKSo3Bp8wyM2E8MUmVXGw0zKyjWHsi7S+lA6SvIgyp1atWmzdupWvvvoKV1dX9uzZwwcffGDpsIQQplAUaHxj4O6+78DEBSkHNquCh5Md566ms/KgtL5YO0leRJmk0WgYNWoUUVFRDBo0iJkzZxqfy83NtWBkQogChT0Odi5w9RSc22lSFUc7G55tYxj78u22GBm0b+UkeRFlWsWKFVmwYAE+Pj7Ga/3792fw4MEkJiZaMDIhxF3pnCG0t+H88FKTqz3ZuAo6Gw3H4lM5eD7JPLGJYiHJixD/cuTIEX799Vd++OEHQkJCWLhwofyFJkRJVPcJw+PRlZCTaVIVN0dbetSpAMBPsmGjVZPkRYh/qV27Njt27KB27dokJiby9NNP06VLF06fPm3p0IQQ/1a1FbhWgqxkOLHO5GoDm1YB4LfDF0nOyDFXdMLMJHkR4j+aNWvGgQMHeO+999DpdKxfv57atWvz4YcfkpMjv+yEKBE0Ggh7zHBeiK6jBlXKUcPHmcwcPavCZeCutZLkRYg7sLW1ZcKECURERNChQwcyMjL44osvZJNHIUqSOje6jk6sM3nNF0VRGNDE0PqyePc56Ra2UpK8CHEP1atXZ8OGDSxYsIBvvvkGZ2dnAPR6PWlpBe9sK4QwI59a4FMb8rIhcpXJ1R6pX1kG7lo5SV6EKICiKAwaNIguXboYr82bN4/Q0FB+//13C0YmhKDO44bHQnQduTna0rNORcDQ+iKsjyQvQhSSqqrMmTOH8+fP06tXLx5//HHi4uIsHZYQZVNYP0AxrPdy7YzJ1QY09QPgdxm4a5UkeRGikBRFYevWrfzvf/9Dq9WybNkyQkJC+Oabb9CbuNqnEKKIuFaEgDaG84hlJldrUKUcNX1cyMzRy4q7VkiSFyHug6OjIx988AH79u2jUaNGJCcn8+yzz9KuXTuOHz9u6fCEKFtuDtw9/DOYOABXURT6NzG0vvy0RwbuWhtJXoR4APXq1WPXrl188sknODk5sX37dpKTky0dlhBlS0gvsLGHxOMQF25ytUfqV8be1jBwNyJW/t9aE0lehHhAWq2Wl19+mcjISL755hsaN25sfC42VpqjhTA7e1eo2d1wfmiJydXcHG3pEOwNwNoj8eaITJiJJC9CFJEqVaowbNgw49dRUVFUq1aNkSNHkpSUZLnAhCgL6vY3PB5eavJ2AQBdaxu2C1h7JF66jqyIJC9CmMlff/1FVlYWc+fOJSQkhGXLlskvRyHMJagjuFaGjGsQ9ZvJ1drX9MJOq+F04nVOXJK1m6yFJC9CmMkrr7zCli1bqFmzJvHx8Tz++OM8/PDDnD9/3tKhCVH6aLTQ4GnD+YEFJldzsbeldXVPANZESNeRtZDkRQgzatOmDeHh4UyaNAlbW1t+//13QkNDmT17tqVDE6L0qf8UKBo4sxUST5pcrUttXwDWHpXkxVpI8iKEmdnb2/P2228THh5Oy5YtSUtLkxlJQpiDW2UIeshwXojWl4dCfNBqFKLiUjh75bqZghNFSZIXIYpJaGgo//zzD4sWLeK1114zXj958iTp6ekWjEyIUqThEMNj+GLIzTapSjknO5oFlgdk1pG1kORFiGKk0WgYMGAAtra2AGRlZfHwww8TFhbG+vXrLRydEKVA9c7gUgHSEyH6D5OrGWcdSdeRVZDkRQgLiomJITU1ldOnT9O5c2cGDRrE5cuXLR2WENZLa2MY+wKwf77J1bqE+qAocPBcEnHJGeaJTRQZSV6EsKDg4GAiIyN56aWXUBSFH3/8kZCQEH744QeZVi3E/ar/NKDA6c1wNcakKt6u9jSsUg6AdUcTzBebKBKSvAhhYS4uLnz66afs3LmTsLAwrly5wuDBg3nooYdISUmxdHhCWJ9yVaFaB8P5gR9Mrtb1xqyjNUdkl/iSTpIXIUqIpk2bsn//fqZPn469vT1gSGyEEPeh4WDDY/giyMsxqUqXWobkZU/MVa6kZZkrMlEEJHkRogSxtbVl/PjxREREMG/ePBRFASA5OZk9e/ZYODohrEjN7uDkDWkJcHKDSVX8yjtSu5IrehU2REnXUUkmyYsQJVBQUBD+/v7Gr8ePH0+zZs14+eWXSU1NtVxgQlgLrS2E9TOcF2Kzxm43Zh39fli6jkoySV6EKOH0ej3p6emoqspnn31GaGgov/1m+t4tQpRZdZ80PEavMex5ZIKedQzJy/aTiVxKMX2DR1G8JHkRooTTaDQsWLCAdevWERgYyIULF3j44Yfp168fcXHy16EQd+UbBt61IC8Ljq40qUpVDycaVHFHr8LqQxfNG5+4b5K8CGElHnroISIiIhg3bhxarZZffvmFkJAQNm7caOnQhCiZFOVW60shuo4eaVAZgBUHY80RlSgCkrwIYUUcHR15//332bdvH40aNUKr1VKnTh1LhyVEyRXWz7BZ4/ldcPW0SVV6hlXAVqtw9GIKxxNkjFlJJMmLEFaoXr167Nq1i61bt+Ll5WW8vnjxYrKyZIqnEEauFSCwneH88M8mVSnnZEe7mt6AtL6UVJK8CGGltFotoaGhxq+XL1/OwIEDqVu3Lv/8848FIxOihKnb3/B46CcwceXqvvUrAbDqYCx6vax2XdJI8iJEKWFra4uPjw/R0dG0bduWZ599lmvXTJthIUSpFtwD7Jzh2hk4v9ukKu2DvXGxt+Ficia7Y66aNz5RaGZLXt59911atGiBo6Mj7u7uJtVRVZVJkyZRoUIFHBwc6NSpEydOnDBXiEKUKr169SIqKopnn30WgG+++YaQkBB+/vln2SdJlG12ThDa23B+6CeTqtjbao3TplccvGCuyMR9Mlvykp2dTb9+/Rg1apTJdT788EM+++wzZs+eze7du3FycqJLly5kZspceyFMUa5cOebMmcM///xDcHAwCQkJPPHEE7zwwguWDk0Iy7o56+joCsgx7TOlTz1D19GaiHgyc/LMFZm4D2ZLXt5++21effVVwsLCTCqvqiqffPIJb731Fr1796ZOnTr88MMPXLx4kZUrV5orTCFKpdatWxMeHs6UKVOws7Ojd+/elg5JCMuq2gpcK0NmMhxfa1KVxv7lqeTuQGpWrmwXUMKUmDEvMTExxMfH06lTJ+M1Nzc3mjZtys6dO+9aLysri5SUlHyHEAJ0Oh2TJ08mJiaGLl26GK8vX76c8PBwywUmhCVoNFDnccO5ibOONBqFPvUrArBSZh2VKCUmeYmPjwfAx8cn33UfHx/jc3cyffp03NzcjIefn59Z4xTC2lSsWNF4Hhsby5AhQ2jUqBHjxo0jPT3dgpEJUcxqP2p4PLkBskxbv+WRG7OONkdflp2mS5BCJS/jx49HUZR7HseOHTNXrHc0YcIEkpOTjcf58+eL9f5CWBMbGxu6detGXl4eH374IbVr12bdunWWDkuI4uFTC8pXM2wXcMK0n/sgbxdqV3IlV6+y9ujd/5AWxatQycuYMWOIioq65xEYGHhfgfj6+gKQkJC/XzEhIcH43J3odDpcXV3zHUKIO/Px8WHp0qX89ttv+Pn5GbuUnn76aS5fvmzp8IQwL0WB0IcN55GrTa7Ws46h9fIP2Wm6xChU8uLl5UVwcPA9Dzs7u/sKJCAgAF9f33z7tKSkpLB7926aN29+X68phLiznj17EhkZySuvvIJGo2HhwoWEhYWRmipLoYtSLuRG8nJiHWSb1m3aI8wwZXrX6StcTpWuo5LAbGNezp07R3h4OOfOnSMvL4/w8HDCw8NJS0szlgkODmbFihUAKIrCK6+8wjvvvMPq1auJiIhg0KBBVKxYkT59+pgrTCHKLGdnZ2bNmsWuXbuoW7cuTz/9NC4uLpYOSwjzqlgf3KpATjqcMm1TU7/yjtT1M+w0LV1HJYONuV540qRJLFiwwPh1/fr1Adi0aRPt2rUDIDo6muTkZGOZ//3vf1y/fp1nn32WpKQkWrVqxdq1a7G3tzdXmEKUeY0bN2bv3r3k5d1ax+Lo0aOsXr2asWPHYmtra8HohChiN7uOdn5h6DoK6WVStZ5hFTh0Pok/Dl/k6WZVzRykKIiilrKlN1NSUnBzcyM5OVnGvwhxH/R6Pa1bt2bHjh2EhYXxzTff0LRpU0uHJUTRObcbvusMOld4/STY6AqscuFaOq0+2ISiwO43OuLtIn9UF7XCfH6XmKnSQoiSQVEUnnvuOTw8PIiIiKB58+a89NJLMh5GlB6VG4NLBchKgdObTatSzpH6VdxRVcOKu8KyJHkRQuSjKApPP/00x44dY9CgQaiqyueff05oaCirV5s+Q0OIEkujudVdFLnK5Go3B+7KrCPLk+RFCHFHnp6eLFiwgPXr1xMYGMiFCxfo3bs3q1aZ/steiBLr5kaNx/6AvByTqnS/kbzsPXuV+GTZc8+SJHkRQtxTp06diIiIYPz48TRt2pQePXpYOiQhHlyV5uDkBZlJcGarSVUqujvQsGo5Q9fREWl9sSRJXoQQBXJ0dGT69Ols27YNGxvDJMXMzEyefvppjh49auHohLgPGi0E30jEpevI6kjyIoQw2c3EBeCDDz5g4cKF1K9fn0mTJpGZKc3owsr8u+tIn3fvsjd0D6uAosC+s9e4mJRhxuDEvUjyIoS4L8OGDaNXr17k5OQwbdo06tWrx5YtWywdlhCm828NDuXg+mU4t9OkKr5u9jSuWh6APyOk9cVSJHkRQtwXPz8/Vq1axbJly/D19SU6Opp27doxYsQIrl27ZunwhCiY1hZqdDWcH/vT5Go96hi6jpbtu0ApWyrNakjyIoS4b4qi8NhjjxEVFcXIkSMBmDdvHqNHj7ZwZEKYqGZ3w2P0H2BiItKnfiWc7LREJ6Sy+bhsaGoJkrwIIR6Yu7s7s2fPZuvWrTRq1Ih33nnH0iEJYZpqHUCrg2tn4FKUSVXcHGx5skkVAOZuOW3G4MTdSPIihCgyrVq1Ys+ePQQEBBivvfLKK8yaNYvc3FwLRibEXeicoVp7w/mxP0yuNqxVAFqNws7TV4i4kFxwBVGkJHkRQhQpRVGM5/v27ePTTz/ltddeo1mzZhw8eNCCkQlxF//uOjJRJXcHet0Y+zLnn1PmiErcgyQvQgizadCgAXPnzsXNzY39+/fTuHFjXn/9da5fv27p0IS4pWY3QIGLByE51uRqz7apBhhmHZ2/mm6m4MSdSPIihDAbjUbDiBEjOHbsGI8//jh5eXl8/PHH1K5dm7/++svS4Qlh4OwNfk0M59GmzzoKrehK6+qe6FX4dluMmYITdyLJixDC7Hx9fVm6dCm//fYbfn5+nDlzhqeffpq0tDRLhyaEwc2uo0KMewEYeaP1Zene81y7nl3UUYm7kORFCFFsevbsSWRkJK+88gqffvopzs7OAKiqKutlCMsK7ml4PLMNMk0fgNsyyIPQCq5k5OTx466zZgpO/JckL0KIYuXs7MysWbPo37+/8dqyZcvo2LEjJ06csGBkokzzDALPGqDPgRPrTa6mKAoj2wYCsGDHGTJzTNtmQDwYSV6EEBaVl5fH+PHj2bRpE2FhYbz33ntkZ0vzu7CA++w66h5WgUruDly5ns2Kg6YP+BX3T5IXIYRFabVaNmzYwEMPPURWVhZvvvkmDRs2ZNeuXZYOTZQ1N7uOTqyH3CyTq9lqNQxt6Q8YBu7q9dIFam6SvAghLC4wMJC//vqLhQsX4unpyZEjR2jRogWjR48mJSXF0uGJsqJSQ3D2gexUOLO1UFWfaOyHs86Gk5fS2HJCtgwwN0lehBAlgqIoDBw4kKioKAYPHoyqqnz55Zfs27fP0qGJskKjubVRY+TqQlV1sbflycZ+AMzbKlsGmJskL0KIEsXT05P58+ezYcMG3nzzTTp06GB8TsbCCLOr/ajh8fBSSE0oVNUhLf3RahS2n7xC5EVpMTQnSV6EECVSx44d823weO7cOQIDA/n666/R6/UWjEyUagFtDN1HuZmw8/NCVa1czpFutX0BWbTO3CR5EUJYhS+//JLY2Fief/55WrduzdGjRy0dkiiNFAXajjOc7/0Orl8pVPVnWhumTa8+FMullMyijk7cIMmLEMIqvPfee3z++ec4OzuzY8cO6tevz8SJE8nMlA8IUcSqd4YKdSHnOuz6slBV6/m506hqOXLyVBbsPGOe+IQkL0II66DVahk9ejSRkZE8/PDD5OTk8M4771CnTh02b95s6fBEaaIo0OZ1w/nuuZB+tVDVb7a+LNp9jvTs3KKOTiDJixDCyvj5+bFy5Up+/fVXKlSowIkTJ2STR1H0avYA71qGadO75xSq6kOhPlQp70hSeg6/HpBF68xBkhchhNVRFIW+ffsSFRXFm2++yaRJk4zPJSUlyT5J4sFpNNBmrOF899eQafrsIa1GYdiNResW7DgjP49mIMmLEMJqubm58c477+Dg4AAYthro3LkzPXr04MyZM5YNTli/0N6G/Y4yk2HP3EJVfbRhZRxstZy8lMa+s9fMFGDZJcmLEKLUOHDgAIcOHWLNmjXUqlWLmTNnkpsrYw7EfdJob4192fkFZKWZXNXF3paH61YE4Kfd58wRXZkmyYsQotRo3Lgxhw8fpk2bNqSnpzNmzBiaNm3KgQMHLB2asFa1+kI5f8i4BtFrClW1f9MqAPweEUdSuiywWJQkeRFClCo1a9Zk06ZNzJs3D3d3dw4cOEDjxo0ZO3asTKsWhae1gdqPGc4jVxaqat3KboRUcCU7V89yGbhbpCR5EUKUOhqNhuHDhxMVFcUTTzyBXq9n69at2NraWjo0YY1qPWJ4PLEeslJNrqYoCgNutL78tOecDNwtQpK8CCFKLV9fX5YsWcIff/zBvHnz0Gq1AGRkZHDp0iULRyeshk8t8AiCvCyIXluoqr3rVcTBVssJGbhbpCR5EUKUet27dycsLMz49dSpUwkODub777+Xv4ZFwRQFQvsYzgvZdeRqb0uvuhUAGbhblCR5EUKUKbm5ufz9999cu3aNYcOG0aFDB44fP27psERJV6uP4bGQXUcA/ZvIwN2iJsmLEKJMsbGxYfv27Xz00Uc4ODiwefNm6tSpwzvvvEN2tnywiLvwqX2r6+h44VZ0rufnLgN3i5gkL0KIMsfGxoaxY8dy9OhRunTpQlZWFhMnTqR+/focPnzY0uGJkujfXUdHVxSyqsKAJn6ADNwtKpK8CCHKrICAANasWcOiRYvw8vLi7NmzuLu7WzosUVI9QNdR7/qVjAN3D5xLKvLQyhpJXoQQZZqiKAwYMICoqChWrFhBlSpVjM/t37/fgpGJEsenNpSvdl9dR672tnSt7QvAb4cumiO6MkWSFyGEADw8PHjooYeMX2/cuJFGjRrRt29fYmNlnILA0HV0s/WlkF1HgHHW0e+H48jTS9fRg5DkRQgh7iAyMhIbGxtWrFhBSEgIX331FXq93tJhCUu7uWDdyQ2F7jpqFeSFu6MtiWlZ7Dp9xQzBlR2SvAghxB28+OKLHDhwgKZNm5KamsoLL7xAq1atOHLkiKVDE5Z0s+soN7PQXUd2Nhq6SddRkZDkRQgh7iIsLIzt27fzxRdf4OLiws6dO6lfvz4zZsywdGjCUv7ddXRkeaGr97qx0/SaI/Fk50pL3v2S5EUIIe5Bq9XywgsvEBkZSe/evcnNzcXPz8/SYQlLurlR44l1kH61UFWbBnjg7aIjOSOHrScumyG4ssFsycu7775LixYtcHR0NHnq4ZAhQ1AUJd/RtWtXc4UohBAmq1y5MitXrmTr1q3069fPeH3Hjh1cuSLjF8oUn1DwrQP6HDjya6GqajUKPeoYBu5K19H9M1vykp2dTb9+/Rg1alSh6nXt2pW4uDjj8dNPP5kpQiGEKLxWrVqhKAoAV65coU+fPoSEhLB48WJZfKwsqTfA8Bi+uNBVb3YdrYtMICM7ryijKjPMlry8/fbbvPrqq/k2QzOFTqfD19fXeJQrV85MEQohxIO5dOkSXl5eXL58mYEDB9KtWzdiYmIsHZYoDrUfA40NXDwAl6MLVbW+nzuVyzmQnp3H38dkd/P7UeLGvGzevBlvb29q1qzJqFGjCmyOzcrKIiUlJd8hhBDFISQkhIMHDzJt2jTs7Oz466+/qF27NjNmzCA3N9fS4QlzcvaCoBvrAh1aUqiqiqIYW19WH5I1hO5HiUpeunbtyg8//MDGjRv54IMP2LJlC926dSMv7+7NatOnT8fNzc14yEA6IURxsrOz46233uLw4cO0bduW9PR0xo4dS7NmzcjIyLB0eMKc6j5peDy8FPSF6/7pVceQvGyKvkxKZk5RR1bqFSp5GT9+/G0Dav97HDt27L6DefLJJ3n44YcJCwujT58+/P777+zdu5fNmzfftc6ECRNITk42HufPn7/v+wshxP2qWbMmmzZt4ttvv6VcuXLUr18fBwcHS4clzKlmN7B3g5RYOLO1UFVDKrgQ5O1Mdq6edUcTzBRg6WVTmMJjxoxhyJAh9ywTGBj4IPHc9lqenp6cPHmSjh073rGMTqdDp9MV2T2FEOJ+KYrCsGHD6NmzJ7a2tsbrZ86cITIyku7du1swOlHkbHRQ+1HY9x2E/wSB7UyuqigKvepUZNaG46w4eIHHGlY2X5ylUKGSFy8vL7y8vMwVy20uXLjAlStXqFChQrHdUwghHpS3t7fxXFVVRo0axdq1a3nyySf55JNP8PHxsWB0okjV7W9IXqJWQ9YM0DmbXLVvg0p8uvE4209e4XhCKjV8XMwYaOlitjEv586dIzw8nHPnzpGXl0d4eDjh4eGkpaUZywQHB7NihWFzq7S0NF5//XV27drFmTNn2LhxI7179yYoKIguXbqYK0whhDCr3NxcatWqhUajYcmSJQQHB/Ptt9/KtOrSonJjw3YBOekQ9VuhqvqVd6RzqGG7gO+3yyy1wjBb8jJp0iTq16/P5MmTSUtLo379+tSvX599+/YZy0RHR5OcnAwYVrE8fPgwDz/8MDVq1GD48OE0bNiQrVu3SreQEMJq2dra8vHHH7Nnzx7q169PUlISzzzzDO3bt+f48eOWDk88KEUxtL4AHCr8mi/DWwcAsPxALFevZxdlZKWaopay9D8lJQU3NzeSk5NxdXW1dDhCCGGUm5vLp59+yqRJk0hPT0en07F27VratWtn6dDEg0g6B5+EAQq8fAjKVTW5qqqqPPzFdiJikxnbuQajO1Q3X5wlXGE+v0vUVGkhhCjNbGxsGDNmDEeOHKFLly5UrVqVZs2aWTos8aDcq0Bge0CFnV8WqqqiKAxvZWh9+WHnWdms0USSvAghRDELCAhgzZo1bN26FXt7e8DQKvPuu+8au9KFlWn1iuHxwAJIK9yGi93DKuDtouNSahZ/RMh+R6aQ5EUIISxAUZR8s5I+++wz3nrrLUJCQli+fLkM6LU2AW2hYgPIzYTdXxeqqp2NhsEt/AH4dluM/NubQJIXIYQoARo0aED16tWJi4vj0Ucf5ZFHHuHChQuWDkuYSlGg9RjD+Z5vILNwLWj9m1RBZ6PhSGwKe89cM0OApYskL0IIUQK0a9eOw4cP89Zbb2FjY8OqVasIDQ3liy++uOcWKaIEqdkdvIIhKwX2fluoquWd7OjbwLBQ3XfbZNp0QSR5EUKIEsLe3p5p06Zx8OBBmjVrRmpqKi+++CIvvfSSpUMTptBooNVrhvOdX0J2eqGqD2vpD8C6yHjOXy1c3bJGkhchhChhateuzfbt2/nyyy/x8PDg+eeft3RIwlS1HzXMPkpPhIMLC1W1uo8Lrat7oldh0e5zZgqwdJDkRQghSiCNRsPzzz/PuXPnqFWrlvH6zJkz+fvvvy0YmbgnrQ20fNlwvuMzyCvcjtFPNTOsEfPzvvNk5Up34d1I8iKEECWYo6Oj8fzgwYP873//o2PHjgwdOpQrV65YMDJxV/WeAidvSD4PEcsKVbVjsDcV3Oy5ej2bNRHxZgrQ+knyIoQQVqJatWo899xzKIrC/PnzCQ4OZtGiRTK1tqSxtYfmLxjOd34Fhfj3sdFqeLJxFQAW7jprjuhKBUlehBDCSri6uvLFF1+wfft2atWqRWJiIk899RRdu3bl9OnTlg5P/FvDwWBjDwkRcPFAoao+2cQPG43CvrPXOBafYqYArZskL0IIYWWaN2/OgQMHeOedd9DpdKxbt46WLVuSlZVl6dDETQ7lILS34Xz//EJV9XG1p3MtH0BaX+5GkhchhLBCdnZ2vPnmmxw+fJh27doxceJEdDqdpcMS/9ZgsOEx4lfISi1U1aeaGgburjgQS1pWblFHZvUkeRFCCCtWo0YN/v77b5577jnjtXXr1vHaa6+RlpZmwcgEVVuAR3XIuQ5Hfi1U1ebVPAj0cuJ6dh4rD8aaKUDrJcmLEEJYOUVR0GgMv86zs7N57rnnmDVrFrVq1eLPP/+0cHRlmKJAg0GG8/0LCllVYeCN1peFu87KoOz/kORFCCFKETs7O7766iv8/f05d+4cPXr04MknnyQ+XqbdWkS9AaCxNQzajY8oVNXHGlTG3lbDsfhU9p+V/Y7+TZIXIYQoZbp27cqRI0cYO3YsGo2GpUuXEhISwrx589Dr9ZYOr2xx8oTgHobzQra+uDna8nDdioCsuPtfkrwIIUQp5OTkxEcffcTevXtp0KABSUlJjBgxgk2bNlk6tLKn4Y2Bu4d/LvR+R/2bGNZ8WXsknusycNdIkhchhCjFGjRowO7du5k5cyYDBw6kY8eOlg6p7AloB+5VISsZIlcWqmo9P3f8PRzJyMljXaR0/d0kyYsQQpRyNjY2vPrqqyxceGujwMuXL9OmTRu2bdtmwcjKCI0GGjxtOL+Pgbt96lcCYOXBi0UdmdWS5EUIIcqgyZMns3XrVlq3bs1zzz1HUlKSpUMq3eo9BYoWzu+C+COFqtqnniF52XriMpdTZSFCkORFCCHKpHfeeYdnnnkGgDlz5hASEsIvv/wiU3LNxbUChPQynG+eXqiq/p5O1PVzR6/C74el9QUkeRFCiDKpfPnyfPPNN2zatIkaNWoQHx9Pv3796N27N+fPn7d0eKVTuwmgaODY73Bhf6Gq9qlnmHW0MlySF5DkRQghyrR27dpx6NAhJk6ciK2tLb/99hsff/yxpcMqnbyDoc6ThvO/pxWqas86FdFqFA6dTyIm8boZgrMukrwIIUQZZ29vz9SpUzl48CB9+/Zl6tSpxudkXZgi1m6cYdG605sgZqvJ1bxcdLQM8gRgVbhsFyDJixBCCABq1arFr7/+ipubGwCqqtKjRw8mTJhARkaGhaMrJcr531r35e9pUIgxRje7jlaFXyzzY5MkeRFCCHFHmzZtYu3atbz//vuEhYWxYcMGS4dUOrR5HWwc4PxuOLHO5Gqda/lib6shJvE6hy8kmzHAkk+SFyGEEHfUoUMHVqxYQaVKlTh16hQPPfQQQ4YMITEx0dKhWTcXX2gywnC+cRqY2DXnrLPhoVBfAFaW8a4jSV6EEELcVZ8+fYiMjGT06NEoisKCBQsICQlh4cKFZb7r4oG0ehXsXCAholCr7t7sOvrtUBy5eWV3PJIkL0IIIe7J1dWVzz//nB07dlC7dm0SExN57733yMnJsXRo1suxPLR40XD+z0cmj31pU8OLco62JKZlsfHYJTMGWLJJ8iKEEMIkzZo148CBA7z33nvMnTsXOzs7AHJzcyWRuR9NR4KtE1yKhJh/TKpiq9Xw5I3NGj/beKLMtn5J8iKEEMJktra2TJgwgVatWhmvzZgxg8aNG7N3714LRmaFHNyhXn/D+a6vTa42onUgjnZajl5MYWNU2Wx9keRFCCHEfcvKyuKLL77g0KFDNGvWjFdeeYXU1FRLh2U9mj5neDy+Fq6eNqlKeSc7BjX3B+CTjcfLZOuLJC9CCCHum06nY//+/QwcOBC9Xs+nn35KrVq1+OOPPywdmnXwrA5BDwEq7J5rcrURrQNwtNNyJDaFv8vg2BdJXoQQQjwQb29vFi5cyNq1awkICOD8+fP07NmTJ554gvj4eEuHV/I1u9H6cnAhZKaYVMXDWXer9WVD2Rv7IsmLEEKIItGlSxciIiJ4/fXX0Wq1rFy5kqSkJEuHVfJV6wieNSA7FcIXm1xtROsAHGy1RMQmsym6bLW+SPIihBCiyDg5OfHhhx+yb98+Zs+eTXBwsPE5WdzuLhTl1tiX3bNBn2dSNQ9nHYNaVAXKXuuLJC9CCCGKXL169Rg6dKjx6z179lClShXefvttsrKyLBhZCVX3SbB3g2sxhdoy4NnWgTjYajl8oWy1vkjyIoQQwuyWLl1KRkYGU6ZMoV69emzdavqOymWCnRM0uLFhYyGmTRvGvhhaX77efMockZVIkrwIIYQwu48//pilS5fi4+PDsWPHaNOmDSNHjpQxMf/W5FlQtBCzBRIiTa42rFUAGgX2nrnGuSvpZgyw5JDkRQghhNkpisLjjz9OVFQUI0YYNiWcO3cuISEhrFq1ysLRlRDufhDS03C+Z47J1Xxc7WkZ5AmUnQ0bJXkRQghRbMqVK8fcuXPZsmULNWvWJD4+nkuXys5YjQLdHLh7aCmkXzW5Wp96lQBYeTC2TAzcleRFCCFEsWvTpg2HDh1i7ty5DB8+3Hj99OnT5OWZNtumVKrSHHzDIDcDDvxgcrUutX2xt9VwOvE6hy4kmzHAkkGSFyGEEBah0+kYMWIEGo3hoygtLY127drRvHlzDh06ZOHoLERRoOkow/neeZCXa1I1Z50NXWr5AobWl9JOkhchhBAlwqFDh0hOTmbv3r00bNiQ8ePHk55eNgag5lP7UXD0gOTzEP2nydX61Dd0Hf126CI5eXpzRVcimC15OXPmDMOHDycgIAAHBweqVavG5MmTyc7Ovme9zMxMXnjhBTw8PHB2dubRRx8lISHBXGEKIYQoIVq2bElUVBSPPvooeXl5fPDBB4SFhbF+/XpLh1a8bO2h4Y01cnbPNrla6yBPPJ3tuHI9m60nLpspuJLBbMnLsWPH0Ov1zJkzh6NHjzJr1ixmz57NG2+8cc96r776Kr/99hvLli1jy5YtXLx4kb59+5orTCGEECVIxYoV+eWXX1i1ahWVKlXi9OnTdO7cmUGDBpGTk2Pp8IpP4+GGadNnt0PcYZOq2Gg19KpbEYAVBy+aMzqLM1vy0rVrV77//ns6d+5MYGAgDz/8MGPHjmX58uV3rZOcnMy3337LzJkz6dChAw0bNuT7779nx44d7Nq1y1yhCiGEKGEefvhhIiMjefHFF1EUhYyMDGxtbS0dVvFxrQihvQ3nhZg2/ciNrqN1R+NJzSy9yV6xjnlJTk6mfPnyd31+//795OTk0KlTJ+O14OBgqlSpws6dO+9YJysri5SUlHyHEEII6+fq6spnn33Gzp07+eyzz4zXL126xKlTZWA12WY3Bu4eXgbXTdsXKqySG4FeTmTl6ll7pPTu6F1sycvJkyf5/PPPGTly5F3LxMfHY2dnh7u7e77rPj4+d91Wffr06bi5uRkPPz+/ogxbCCGEhTVt2pQKFSoYv37ppZeoXbs2H3zwQenuSqrcGCrWh7ws2P+9SVUUReGRm2u+lOIF6wqdvIwfPx5FUe55HDt2LF+d2NhYunbtSr9+/YwrKxaVCRMmkJycbDzOnz9fpK8vhBCi5MjIyCAxMZHMzEzGjx9Po0aN2LNnj6XDMo9/7za9Zx7kmrah5c1ZRztOXSEuOcNc0VlUoZOXMWPGEBUVdc8jMDDQWP7ixYu0b9+eFi1aMHfu3Hu+tq+vL9nZ2bftdZGQkICvr+8d6+h0OlxdXfMdQgghSicHBwfWr1/PggUL8PDw4PDhwzRr1oyXX36Z1NRUS4dX9Gr1BZeKkBYPh5eaVMWvvCON/cuhqrCylA7cVVQzriMcGxtL+/btadiwIQsXLkSr1d6zfHJyMl5eXvz00088+uijAERHRxMcHMzOnTtp1qxZgfdMSUnBzc2N5OTkeyYyeXl5pbu5UZQYdnZ2xkW4hBBF5/Lly4wZM4Yff/wRgMqVK/P7779Tt25dC0dWxHZ8DuveAo/q8MIeMOH3yZI95xi/PIIgb2fWv9oGRVGKIdAHY+rnN5gxeYmNjaVdu3ZUrVqVBQsW5EtcbraixMbG0rFjR3744QeaNGkCwKhRo/jzzz+ZP38+rq6uvPjiiwDs2LHDpPsW9M2rqkp8fLzsZCqKjUajISAgADs7O0uHIkSptH79ep577jmys7OJjIzExcXF0iEVraxUmFkLspLhiUW3Nm+8h5TMHJq8u4HMHD0rX2hJPT9388f5gAqTvNiYK4j169dz8uRJTp48SeXKlfM9dzNfysnJITo6Ot8KirNmzUKj0fDoo4+SlZVFly5d+Oqrr4osrpuJi7e3N46OjlaRjQrrpdfruXjxInFxcVSpUkV+3oQwg4ceeoiIiAhOnTplTFxUVWXlypX07t3b+ls+dS6GdV+2zYTtn0BwD8N4mHtwtbelay1fVoZf5Jf9560ieSkMs3YbWcK9Mre8vDyOHz+Ot7c3Hh4eFopQlDXJyclcvHiRoKCgsrVOhRAW9N133zF8+HBatWrF3LlzCQkJsXRIDyY1AT4JM8w8GroGqrYosMq2E4k89e1uXO1t2PNmJ+xt7z10w9IK0/Ji5elo4dwc4+Lo6GjhSERZcrO7qEzvlCtEMdPr9Tg5ObFt2zbq1q3LlClTyMoybbZOieTiA/X6G863f2pSlebVPKjgZk9KZi4boy6ZMbjiV6aSl5uk6V4UJ/l5E6L4PfPMM0RGRtKjRw9ycnJ4++23qVu3Lv/884+lQ7t/LV4CFDi+FhIiCyyu1Sj0bWCYNv3L/tK1jEiZTF6EEEKUflWqVOG3337j559/xsfHh+joaNq2bcv06dMtHdr98agGIb0M5zs+N6nKow0MY063HL/MpZRMc0VW7CR5EUIIUWopikK/fv2Iiori2WefRaPR0L59e0uHdf9avWJ4jPgZki8UWDzQy5kGVdzRq6VrxV1JXoQowObNm1EURabXC2HFypUrx5w5czhx4kS+NcN++eUXzp07Z8HICqlSQ/BvDfpc2PW1SVUea2jYNueX/RcoLXN0JHmxEkOGDEFRFN5///1811euXGn2MRVnzpzJt/2Dh4cHnTt35uDBg2a9rzlNmTKFevXqmVS2RYsWxMXF4ebmZt6ghBBm9+8V4I8fP85TTz1FaGgon376qfUMqm/5iuFx/3zISCqweI86FdDZaDiekEZEbLI5Iys2krxYEXt7ez744AOuXbtmkftv2LCBuLg4/vrrL9LS0ujWrdt9t0ZkZ2cXbXBmkpOTg52dHb6+vjLwVohSRqPR0LhxY65fv84rr7xCs2bNCA8Pt3RYBQvqCN61IDsN9n1XYHE3B1u61DIsDvvL/oK7mqxBmU9eVFUlPTvXIkdhm+86deqEr6/vPQeb/frrr9SqVQudToe/vz8zZszI97y/vz/vvfcew4YNw8XFhSpVqhS459RNHh4e+Pr60qhRIz7++GMSEhLYvXs3p06donfv3vj4+ODs7Ezjxo3ZsGHDbfedNm0agwYNwtXVlWeffRaAcePGUaNGDRwdHQkMDGTixIn5tm242ULy3XffUaVKFZydnXn++efJy8vjww8/xNfXF29vb959991890tKSuKZZ57By8sLV1dXOnTowKFDhwCYP38+b7/9NocOHTK2Js2fPx8w9I9//fXXPPzwwzg5OfHuu+/esdto+/bttGvXDkdHR8qVK0eXLl0sllQKIe5PUFAQW7ZsYc6cObi5ubFv3z4aNWrEuHHj8i2eWuIoCrR82XC+ezbkFDwQ9+asoz8j4tHrrb/ryGwr7FqLjJw8Qif9ZZF7R07tgqOd6f8EWq2W9957jwEDBvDSSy/dtnLx/v37efzxx5kyZQpPPPEEO3bs4Pnnn8fDw4MhQ4YYy82YMYNp06bxxhtv8MsvvzBq1Cjatm1LzZo1TY7FwcEBMLSgpKWl0b17d9599110Oh0//PADvXr1Ijo6mipVqhjrfPzxx0yaNInJkycbr7m4uDB//nwqVqxIREQEI0aMwMXFhf/973/GMqdOnWLNmjWsXbuWU6dO8dhjj3H69Glq1KjBli1b2LFjB8OGDaNTp040bdoUgH79+uHg4MCaNWtwc3Njzpw5dOzYkePHj/PEE09w5MgR1q5da0yy/t0lNGXKFN5//30++eQTbGxsOH36dL7vPTw8nI4dOzJs2DA+/fRTbGxs2LRpk/U0OQshjDQaDc8++yy9evXi5ZdfZtmyZXz44Yf8/vvvHDp0CBubEvoxWbsvbJwKKRcMGzY2HHzP4i2qeeJkpyUxLYuI2GTqWvmKu2W+5cXaPPLII9SrVy9fAnDTzJkz6dixIxMnTqRGjRoMGTKE0aNH89FHH+Ur1717d55//nmCgoIYN24cnp6ebNq0yeQYkpKSmDZtGs7OzjRp0oS6desycuRIateuTfXq1Zk2bRrVqlVj9erV+ep16NCBMWPGUK1aNapVqwbAW2+9RYsWLfD396dXr16MHTuWn3/+OV89vV7Pd999R2hoKL169aJ9+/ZER0fzySefULNmTYYOHUrNmjWN38O2bdvYs2cPy5Yto1GjRlSvXp2PP/4Yd3d3fvnlFxwcHHB2dsbGxgZfX198fX2NyRjAgAEDGDp0KIGBgfmSr5s+/PBDGjVqxFdffUXdunWpVasWo0ePxtPT0+T3UAhRslSoUIGff/6Z1atXU7lyZQYOHFhyExcArS00f8FwvuMz0OvvWdzORkObGl4AbDxm/QvWleB/meLhYKslcmoXi937fnzwwQd06NCBsWPH5rseFRVF7969811r2bIln3zyCXl5ecbNMevUqWN8XlEUfH19uXTJ8MPcrVs3tm7dCkDVqlU5evSosWyLFi3QaDRcv36dwMBAli5dio+PD2lpaUyZMoU//viDuLg4cnNzycjIuG0Ef6NGjW77XpYuXcpnn33GqVOnSEtLIzc397Zlof39/fNttObj44NWq823X4mPj4/xezh06BBpaWm3bQGRkZHBqVOn7vSWFhjnv4WHh9OvX78CX0cIYX169epFu3bt0Ol0xmu7d+/m2LFjDBo0qGSNfWswCLa8D1dOQvSfBW7Y2CHYmzVH4tl07BKvPVSjmII0jzKfvCiKUqium5KgTZs2dOnShQkTJuTrDjLVf/fXURQF/Y2sfd68eWRkZNyx3NKlSwkNDcXDwwN3d3fj9bFjx7J+/Xo+/vhjgoKCcHBw4LHHHrttUK6Tk1O+r3fu3MnAgQN5++236dKlC25ubixZsuS2cTp3ivde30NaWhoVKlRg8+bNt33v/477bv4b53/9u5VGCFH6/PuPpezsbJ555hmOHDnCjz/+yOzZswkKCrJgdP+ic4bGz8DWGSZt2NiupjeKAhGxySSkZOLjal98sRYx6TayUu+//z6//fYbO3fuNF4LCQlh+/bt+cpt376dGjVqGFtdClKpUiWCgoIICgqiatWq+Z7z8/OjWrVqtyUA27dvZ8iQITzyyCOEhYXh6+vLmTNnCrzXjh07qFq1Km+++aaxe+fs2bMmxXkvDRo0ID4+HhsbG+P3cvO42bVjZ2d332NU6tSpw8aNGx84TiFEyacoCk899RT29vZs3LiRsLAw3n///XwTCyyqyUjQ6uDCXji3655FvVx01K3sDsAmK+86kuTFSoWFhTFw4EA+++wz47UxY8awceNGpk2bxvHjx1mwYAFffPHFbd1LRa169eosX76c8PBwDh06xIABA4ytIAXVO3fuHEuWLOHUqVN89tlnrFix4oHj6dSpE82bN6dPnz6sW7eOM2fOsGPHDt5880327dsHGLqiYmJiCA8PJzExsVAbtk2YMIG9e/fy/PPPc/jwYY4dO8bXX39NYmLiA8cuhChZbG1tGTduHEeOHKFTp05kZmYyYcIEGjVqxJ49eywdXv4NG7fNLLB4x2BvwPrHvUjyYsWmTp2aL0lo0KABP//8M0uWLKF27dpMmjSJqVOn3lfXUmHMnDmTcuXK0aJFC3r16kWXLl1o0KBBgfUefvhhXn31VUaPHk29evXYsWMHEydOfOB4FEXhzz//pE2bNgwdOpQaNWrw5JNPcvbsWXx8fAB49NFH6dq1K+3bt8fLy4uffvrJ5NevUaMG69at49ChQzRp0oTmzZuzatWqkj24TwjxQKpVq8a6dev44Ycf8PDw4PDhwzRr1oxdu+7d2lEsWrwEihZOrCuw9aVDiCF52XYikcwc650hqailZa3gG1JSUnBzcyM5Ofm2gZ+ZmZnExMQQEBCAvb319vUJ6yI/d0KULomJiYwZM4bz58+zcePGkjGId/VLcGABVG0JQ/6469gXVVVpPv1v4lMymT+0Me1qehdzoHd3r8/v/5KWFyGEEKIQPD09WbBgAWvWrDEmLikpKYwcOZKLFy9aJqi24wxjX85uh5N3H5OnKIqx9eVvK+46kuRFCCGEuA//nk791ltvMXfuXEJDQ5k9e7ZJ4/6KlFslaDLCcL7x7Xuu+2Ic9xJ1yWo3apTkRQghhHhAw4cPp3HjxiQnJzNq1CjatGlDZGRk8QbR6jWwc4H4wxC58q7FWlTzRGejITYpg+MJacUXXxGS5EUIIYR4QHXr1mXnzp18+umnODk5sX37duNq6JmZBe89VCScPKDFi4bzTe9CXu4diznYaWkZZFg2YuOxhOKJrYhJ8iKEEEIUAa1Wy0svvURkZCQ9e/YkJyeHqVOn8uabbxZfEM2fB0dPw6q74YvuWqzDja6jv6Osc9yLJC9CCCFEEapSpQqrV69m2bJlhIaG5tto1ux0LtB6jOF8ywd33XH6ZvJy4Nw1rl7PvmOZkkySFyGEEKKIKYrCY489RkREhHF9KYDnn3+eJUuWmHegbKNh4FoZUmJh5xd3LFLR3YGQCq7oVdgQaX1dR5K8CCGEEGby7w1k165dy9dff03//v3p2bNnkWyHcke29tBpiuF86wxIvnDHYj3rVABgxcFY88RhRpK8iBKvXbt2vPLKK5YOQwghHkj79u2ZMmUKdnZ2/Pnnn4SGhjJr1ixyc+88sPaBhD0GVVpATjqse+uORXrXqwjArpgrXEzKKPoYzEiSFysxZMgQFEVBURTs7OwICgpi6tSp5vmhLwaKorBy5UqTyi5fvpxp06aZNyAhhDAznU7H5MmTOXToEK1btyY9PZ3XXnuNZs2acfDgwaK9maJA9w9B0cDRFXB6y21FKpdzpElAeVQVVh+y0OJ690mSFyvStWtX4uLiOHHiBGPGjGHKlCl89NFHhX6dvLy84l9A6T5kZxsGkZUvXz7fFvVCCGHNgoOD2bx5M9988w3u7u7s37+fxx57rOj/GPUNg0bDDedrxkHe7TthP1K/EgArrazrSJIXK6LT6fD19aVq1aqMGjWKTp06sXr1ambOnElYWBhOTk74+fnx/PPPk5Z2a+Gh+fPn4+7uzurVqwkNDUWn03Hu3Dn27t3LQw89hKenJ25ubrRt25YDBw7ku6eiKMyZM4eePXvi6OhISEgIO3fu5OTJk7Rr1w4nJydatGjBqVOn8tVbtWoVDRo0wN7ensDAQN5++23jf0x/f38AHnnkERRFMX49ZcoU6tWrx7x58/LtA/TfbqOsrCzGjRuHn58fOp2OoKAgvv322yJ+t4UQwnw0Gg3PPPMMUVFRPP7443z++efGzV2LdDBv+zfA0QMuR8Geb257unvtCthpNRyLTyUqLqXo7mtmkryoKmRft8zxgD+gDg4OZGdno9Fo+Oyzzzh69CgLFizg77//vm1qXnp6Oh988AHz5s3j6NGjeHt7k5qayuDBg9m2bRu7du2ievXqdO/endTU1Hx1p02bxqBBgwgPDyc4OJgBAwYwcuRIJkyYwL59+1BVldGjRxvLb926lUGDBvHyyy8TGRnJnDlzmD9/Pu+++y4Ae/fuBeD7778nLi7O+DXAyZMn+fXXX1m+fDnh4eF3/L4HDRrETz/9xGeffUZUVBRz5szB2dn5gd5LIYSwBF9fX5YuXUr37t2N1+bOncvAgQO5dKkI1mBxLA8dJxnON0+HtPyv6eZoS8cbex1Z08BdG0sHYHE56fBeRcvc+42LYOdU6GqqqrJx40b++usvXnzxxXytEv7+/rzzzjs899xzfPXVV8brOTk5fPXVV9StW9d4rUOHDvled+7cubi7u7NlyxZ69uxpvD506FAef/xxAMaNG0fz5s2ZOHEiXbp0AeDll19m6NChxvJvv/0248ePZ/DgwQAEBgYybdo0/ve//zF58mS8vLwAcHd3x9fXN18M2dnZ/PDDD8Yy/3X8+HF+/vln1q9fT6dOnYyvL4QQpcH169eZMGEC165dY+3atcyYMYPBgwc/2M7V9Z+Gfd9DXDhseBv6fJnv6T71K7HmSDyrwmMZ1zUYraYE7JJdAGl5sSK///47zs7O2Nvb061bN5544gmmTJnChg0b6NixI5UqVcLFxYWnn36aK1eukJ6ebqxrZ2dHnTp18r1eQkICI0aMoHr16ri5ueHq6kpaWhrnzp3LV+7f9W6uVxAWFpbvWmZmJikphibHQ4cOMXXqVJydnY3HiBEjiIuLyxfTnVStWvWuiQtAeHg4Wq2Wtm3bFvBuCSGE9XFycuKvv/6ibt26XL16laFDh9KxY0dOnDhx/y+q0UL3jw3n4YsgIf+eS+1qeuHmYEtCSha7Tl95gOiLj7S82DoaWkAsde9CaN++PV9//TV2dnZUrFgRGxsbzpw5Q8+ePRk1ahTvvvsu5cuXZ9u2bQwfPpzs7GwcHQ33cHBwuC1zHzx4MFeuXOHTTz+latWq6HQ6mjdvbhwoawzT1tZ4fvM17nTt5iDgtLQ03n77bfr27Xvb93BzHMvdODnduyXKwcHhns8LIYS1a9y4MXv37mXWrFlMmTKFTZs2ERYWxqRJk3j99dfz/f41mV9jCHkYolbD3+9A/8XGp3Q2WnrUqcDi3edYcTDWuO9RSSYtL4pi6LqxxFHIZkAnJyeCgoKoUqWKcWDX/v370ev1zJgxg2bNmlGjRg0uXjQtGdu+fTsvvfQS3bt3p1atWuh0OhITEwv9Fv5XgwYNiI6OJigo6Lbj5oJNtra25OXlFfq1w8LC0Ov1bNly+7Q/IYQoLWxtbfnf//5HREQEDz30EFlZWUycOJEjR47c/4t2mGiYOh39B5zfk++pm7OO1h6JJyO78L+bi5skL1YuKCiInJwcPv/8c06fPs2PP/7I7NmzTapbvXp1fvzxR6Kioti9ezcDBw4skpaNSZMm8cMPP/D2229z9OhRoqKiWLJkCW+9dWuhJH9/fzZu3Eh8fDzXrl0z+bX9/f0ZPHgww4YNY+XKlcTExLB582Z+/vnnB45bCCFKmmrVqvHXX3/x448/MmnSJOrXr298rtB/AHrVgHoDDecbpuSbNNKwSjkql3MgLSuXDVElf7sASV6sXN26dZk5cyYffPABtWvXZtGiRUyfPt2kut9++y3Xrl2jQYMGPP3007z00kt4e3s/cExdunTh999/Z926dTRu3JhmzZoxa9YsqlataiwzY8YM1q9fj5+fX77/jKb4+uuveeyxx3j++ecJDg5mxIgRXL9+/YHjFkKIkkhRFJ566ikmT55svBYVFUX16tVNXuzTqN140Org7HY4udF4WaNRjK0v1jDrSFHNujtU8UtJScHNzY3k5GRcXV3zPZeZmUlMTEy+NUSEMDf5uRNCFLWnn36ahQsXAoY1sz7//HMqVapkWuW/3jRs2OgbBs/+Aze6808kpPLQrH+w02o4MOkhnHXFOyz2Xp/f/yUtL0IIIYSVmTt3LhMmTMDGxoYVK1YQGhrK119/bdrq6a3HgM4V4iPg6HLj5SBvZwI8ncjO07Ml+rIZo39wkrwIIYQQVsbBwYH33nuP/fv306RJE1JSUnj++edp3bo1R48evXdlx/LQ4iXD+d/vGLcNUBSFh0INy2Gsj4w3Z/gPTJIXIYQQwkrVqVOHHTt28Nlnn+Hs7MyOHTv4448/Cq7YbBQ4ecG1GDj4o/Fy5xvJy9/HLpGTV3L3wJPkRQghhLBiWq2WF198kcjISF599VVeffVV43N3ncygc4bWYw3n/8yA3CwA6lcph4eTHSmZueyJuWru0O+bJC9CCCFEKeDn58fMmTONi9hlZWXRuHFjnnnmGa5evUMi0nAIuFSElAtw4AcAtBrFuNfRuqMlt+tIkhchhBCiFNqwYQNRUVF8++23hISEsGTJkvw7VtvaQ5sxhvN/PoacDAA6hxr2nFsfmVC0O1wXIUlehBBCiFKoR48ebNu2jZCQEC5dukT//v3p0aMHZ86cuVWo/tPg5gdp8YbNG4FW1T1xsNVyMTmToxdTLBN8ASR5EUIIIUqpli1bcvDgQaZOnYqdnR1r1qyhVq1azJw50zCt2kYHbV43FN42E7KvY2+rpXV1w/5G6yJL5mq7krwIIYQQpZhOp2PixIkcPnyYNm3akJ6eztq1a29t1ltvAJTzh+uXYe88ADrXutV1VBKZLXk5c+YMw4cPJyAgAAcHB6pVq8bkyZNv27H4v9q1a4eiKPmO5557zlxhikLw9/fnk08+MX6tKErhl6YWQghhETVr1mTTpk188803fP3118bkJTU9k+tNXjYU2vYJZKXSIdgbjQJRcSmcv5puuaDvwmzJy7Fjx9Dr9cyZM4ejR48ya9YsZs+ezRtvvFFg3REjRhAXF2c8PvzwQ3OFaTWGDBmSL6Hz8PCga9euHD582GIxxcXF0a1bN4vdXwghROFoNBqeeeYZqlWrZrz2+uuvU3vANNYmeEHGVdg1m/JOdjTyLw+UzNYXsyUvXbt25fvvv6dz584EBgby8MMPM3bsWJYvX15gXUdHR3x9fY1HQXsclBVdu3Y1JnQbN27ExsaGnj17WiweX19fdDqdxe4vhBDiwaSmpvLXX39x5swZus0+xcDl6Vz6Yzqc2W5csK5MJS93kpycTPny5Qsst2jRIjw9PalduzYTJkwgPf3uTVZZWVmkpKTkO0ornU5nTOjq1avH+PHjOX/+PJcvG/agGDduHDVq1MDR0ZHAwEAmTpxITk6Osf6hQ4do3749Li4uuLq60rBhQ/bt22d8ftu2bbRu3RoHBwf8/Px46aWX7rlb87+7jc6cOYOiKCxfvpz27dvj6OhI3bp12blzZ746hb2HEEII83FxcSEiIoJXX30VjUbD4ohcgj+/xvev96ZbBcNn754zV0lKv/eQj+JWbMnLyZMn+fzzzxk5cuQ9yw0YMICFCxeyadMmJkyYwI8//shTTz111/LTp0/Hzc3NePj5+d1XfNevX7/rkZmZaXLZjIwMk8o+qLS0NBYuXEhQUBAeHh6A4Ydw/vz5REZG8umnn/LNN98wa9YsY52BAwdSuXJl9u7dy/79+xk/frxxMaNTp07RtWtXHn30UQ4fPszSpUvZtm0bo0ePLlRcb775JmPHjiU8PJwaNWrQv39/cnNzi/QeQgghio6zszMzZ85k9+7d1Ktbh2uZMOyXKzzVrTkhNpfI06tMXn0Uvb4ErfmiFtK4ceNU4J5HVFRUvjoXLlxQq1Wrpg4fPrywt1M3btyoAurJkyfv+HxmZqaanJxsPM6fP68CanJy8m1lMzIy1MjISDUjI+O25+71/XTv3j1fWUdHx7uWbdu2bb6ynp6edyxXWIMHD1a1Wq3q5OSkOjk5qYBaoUIFdf/+/Xet89FHH6kNGzY0fu3i4qLOnz//jmWHDx+uPvvss/mubd26VdVoNMb3q2rVquqsWbOMzwPqihUrVFVV1ZiYGBVQ582bZ3z+6NGj+X4eTLlHaXSvnzshhChJcnJy1I/emaQ62Cqqh4Oinni/rRo8YZVaddzv6uRVR1S9Xm+2eycnJ9/18/u/bAqb7IwZM4YhQ4bcs0xgYKDx/OLFi7Rv354WLVowd+7cwt6Opk2bAoaWm38PMLpJp9OVmXEX7du35+uvvwbg2rVrfPXVV3Tr1o09e/ZQtWpVli5dymeffcapU6dIS0sjNzc333ih1157jWeeeYYff/yRTp060a9fP+N7eujQIQ4fPsyiRYuM5VVVRa/XExMTQ0hIiEkx1qlTx3heoUIFAC5dukRwcHCR3UMIIYR52NjYMPbNt3n0oRacmj2QoIyDrK32Kx2OP8I3q7fi4WTHix2rWzpMCp28eHl54eXlZVLZ2NhY2rdvT8OGDfn+++/RaArfSxUeHg7c+iA0l7S0tLs+p9Vq83196dKlu5b97/eYbyXDB+Tk5ERQUJDx63nz5uHm5sY333xDjx49GDhwIG+//TZdunTBzc2NJUuWMGPGDGP5KVOmMGDAAP744w/WrFnD5MmTWbJkCY888ghpaWmMHDmSl1566bb7VqlSxeQYb3ZDAcZpeHq9YWfSorqHEEII8wpo0oWAcktgcT+qnl/JOxfWMHDxZT442A7dB5/xbKcwi8ZX6OTFVLGxsbRr146qVavy8ccfGweVgmGWys0yHTt25IcffqBJkyacOnWKxYsX0717dzw8PDh8+DCvvvoqbdq0yfcXvTk4OTlZvGxhKYqCRqMhIyODHTt2ULVqVd58803j82fPnr2tTo0aNahRowavvvoq/fv35/vvv+eRRx6hQYMGREZG5kuOilpx3EMIIUQRqd4JHpkDf71J+JlzqEDsgc281bchFT5+k17D3wCtbYEvYw5mG7C7fv16Tp48ycaNG6lcuTIVKlQwHjfl5OQQHR1tnE1kZ2fHhg0b6Ny5M8HBwYwZM4ZHH32U3377zVxhWpWsrCzi4+OJj48nKiqKF198kbS0NHr16kX16tU5d+4cS5Ys4dSpU3z22WesWLHCWDcjI4PRo0ezefNmzp49y/bt29m7d6+xq2bcuHHs2LGD0aNHEx4ezokTJ1i1alWRDqYtjnsIIYQoQnUeh9ci+WDer6yf0Iag8hoCXfU0Ov8dV9NzCq5vJmZreRkyZEiBY2P8/f3z7Vjp5+fHli1bzBWS1Vu7dq0x+XNxcSE4OJhly5bRrl07AF599VVGjx5NVlYWPXr0YOLEiUyZMgUwdH1duXKFQYMGkZCQgKenJ3379uXtt98GDGNVtmzZwptvvknr1q1RVZVq1arxxBNPFFn8xXEPIYQQRUxrCyE96fReTw6+eJLl333I9To1qODiaLGQFFUtoftd36eUlBTc3NxITk6+bXG7zMxMYmJiCAgIwN7e3kIRirJGfu6EEKJg9/r8/i/ZmFEIIYQQVkWSFyGEEEJYFUlehBBCCGFVJHkRQgghhFWR5EUIIYQQVqVMJi83V3wVojiUsgl9QghhcWZb56UksrOzQ6PRcPHiRby8vLCzszMuYS+EOaiqyuXLl1EUJd/WCUIIIe5fmUpeNBoNAQEBxMXFcfHiRUuHI8oIRVGoXLnybXtkCSGEuD9lKnkBQ+tLlSpVyM3NJS8vz9LhiDLA1tZWEhchhChCZS55AYxN+NKML4QQQlifMjlgVwghhBDWS5IXIYQQQlgVSV6EEEIIYVVK3ZiXm2tqpKSkWDgSIYQQQpjq5ue2KWtjlbrkJTU1FQA/Pz8LRyKEEEKIwkpNTcXNze2eZRS1lC3/qdfruXjxIi4uLkW+AF1KSgp+fn6cP38eV1fXIn1tcYu8z8VD3ufiIe9z8ZH3uniY631WVZXU1FQqVqyIRnPvUS2lruVFo9FQuXJls97D1dVV/mMUA3mfi4e8z8VD3ufiI+918TDH+1xQi8tNMmBXCCGEEFZFkhchhBBCWBVJXgpBp9MxefJkdDqdpUMp1eR9Lh7yPhcPeZ+Lj7zXxaMkvM+lbsCuEEIIIUo3aXkRQgghhFWR5EUIIYQQVkWSFyGEEEJYFUlehBBCCGFVJHkRQgghhFWR5MVEX375Jf7+/tjb29O0aVP27Nlj6ZCs2vTp02ncuDEuLi54e3vTp08foqOj85XJzMzkhRdewMPDA2dnZx599FESEhIsFHHp8P7776MoCq+88orxmrzPRSc2NpannnoKDw8PHBwcCAsLY9++fcbnVVVl0qRJVKhQAQcHBzp16sSJEycsGLH1ycvLY+LEiQQEBODg4EC1atWYNm1avs385H0uvH/++YdevXpRsWJFFEVh5cqV+Z435T29evUqAwcOxNXVFXd3d4YPH05aWpp5AlZFgZYsWaLa2dmp3333nXr06FF1xIgRqru7u5qQkGDp0KxWly5d1O+//149cuSIGh4ernbv3l2tUqWKmpaWZizz3HPPqX5+furGjRvVffv2qc2aNVNbtGhhwait2549e1R/f3+1Tp066ssvv2y8Lu9z0bh69apatWpVdciQIeru3bvV06dPq3/99Zd68uRJY5n3339fdXNzU1euXKkeOnRIffjhh9WAgAA1IyPDgpFbl3fffVf18PBQf//9dzUmJkZdtmyZ6uzsrH766afGMvI+F96ff/6pvvnmm+ry5ctVQF2xYkW+5015T7t27arWrVtX3bVrl7p161Y1KChI7d+/v1nileTFBE2aNFFfeOEF49d5eXlqxYoV1enTp1swqtLl0qVLKqBu2bJFVVVVTUpKUm1tbdVly5YZy0RFRamAunPnTkuFabVSU1PV6tWrq+vXr1fbtm1rTF7kfS4648aNU1u1anXX5/V6verr66t+9NFHxmtJSUmqTqdTf/rpp+IIsVTo0aOHOmzYsHzX+vbtqw4cOFBVVXmfi8J/kxdT3tPIyEgVUPfu3Wsss2bNGlVRFDU2NrbIY5RuowJkZ2ezf/9+OnXqZLym0Wjo1KkTO3futGBkpUtycjIA5cuXB2D//v3k5OTke9+Dg4OpUqWKvO/34YUXXqBHjx753k+Q97korV69mkaNGtGvXz+8vb2pX78+33zzjfH5mJgY4uPj873Xbm5uNG3aVN7rQmjRogUbN27k+PHjABw6dIht27bRrVs3QN5nczDlPd25cyfu7u40atTIWKZTp05oNBp2795d5DGVul2li9r/27l/kLbWMAzgz21OE5FS0yI9Ry0pKQjWP4NGKgfHLN3EzRJK6CK2Bo2FilQc1U4d2kFxsYMW6aBI3SSxgkPT1EZqKE0LgjoYxYokYGnR897t0FyFKjdy7sl9fnAgfN87vDzDlxdyvuzu7uLo6Aiqquasq6qKL1++WNRVYTEMA+FwGM3NzaitrQUApNNpOJ1OuN3unFpVVZFOpy3o0r6mpqbw8eNHxOPxY3vMOX/W1tYwMjKCR48e4cmTJ4jH4+jq6oLT6UQwGDTzPOksYdan19fXh0wmg6qqKjgcDhwdHWFwcBCBQAAAmPM5OE2m6XQa165dy9lXFAVXr149l9w5vJDlOjs7kUwmsbS0ZHUrBWdzcxPd3d2Yn59HUVGR1e0UNMMw0NjYiKGhIQBAfX09kskkRkdHEQwGLe6ucLx+/RqTk5N49eoVampqsLKygnA4jPLycub8P8Kfjf6gtLQUDofj2O2L7e1taJpmUVeFIxQKYW5uDgsLC7h+/bq5rmkafv36hf39/Zx65n42y8vL2NnZQUNDAxRFgaIoWFxcxPPnz6EoClRVZc55UlZWhurq6py1W7duYWNjAwDMPHmW/DuPHz9GX18f2traUFdXh3v37qGnpwfDw8MAmPN5OE2mmqZhZ2cnZ//w8BB7e3vnkjuHlz9wOp3w+XyIRCLmmmEYiEQi0HXdws7sTUQQCoUwMzODaDQKr9ebs+/z+XDx4sWc3FOpFDY2Npj7Gfj9fqyurmJlZcV8GhsbEQgEzM/MOT+am5uPXff/+vUrbty4AQDwer3QNC0n60wmg1gsxqzP4ODgABcu5H51ORwOGIYBgDmfh9Nkqus69vf3sby8bNZEo1EYhoGmpqb8N5X3V4AL0NTUlLhcLnn58qV8/vxZ2tvbxe12Szqdtro123rw4IGUlJTI27dvZWtry3wODg7Mmo6ODvF4PBKNRuXDhw+i67roum5h14Xh99tGIsw5X96/fy+Kosjg4KB8+/ZNJicnpbi4WCYmJsyap0+fitvtltnZWfn06ZO0tLTwCu8ZBYNBqaioMK9KT09PS2lpqfT29po1zPnsstmsJBIJSSQSAkCePXsmiURC1tfXReR0md65c0fq6+slFovJ0tKSVFZW8qq01V68eCEej0ecTqfcvn1b3r17Z3VLtgbgxGd8fNys+fHjhzx8+FCuXLkixcXF0traKltbW9Y1XSD+Obww5/x58+aN1NbWisvlkqqqKhkbG8vZNwxDBgYGRFVVcblc4vf7JZVKWdStPWUyGenu7haPxyNFRUVy8+ZN6e/vl58/f5o1zPnsFhYWTjyTg8GgiJwu0+/fv8vdu3fl0qVLcvnyZbl//75ks9lz6fcvkd/+lpCIiIjoP47vvBAREZGtcHghIiIiW+HwQkRERLbC4YWIiIhshcMLERER2QqHFyIiIrIVDi9ERERkKxxeiIiIyFY4vBAREZGtcHghIiIiW+HwQkRERLbyN+gNeiuAxr6jAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gain_curve_test_non_param = cumulative_gain(cate_test_non_param, \"cate\", y=y, t=T)\n",
+    "plt.plot(gain_curve_test_non_param, color=\"C0\", label=\"Non-Parametric\")\n",
+    "plt.plot(gain_curve_test, color=\"C1\", label=\"Parametric\")\n",
+    "plt.plot([0, 100], [0, elast(test, y, T)], linestyle=\"--\", color=\"black\", label=\"Baseline\")\n",
+    "plt.legend();\n",
+    "plt.title(\"R-Learner\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Caution!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "하지만 Non-Parametric으로 결과를 구할 경우 해석에 주의할 점이 있습니다.\n",
+    "\n",
+    "Non-Parametric은 최종 CATE에서도 비선형 관계를 잡아낼 수 있다는 장점이 있지만, **국소 선형 근사치**만을 찾는 것이기 때문에 \n",
+    "\n",
+    "해당 처치 수준 또는 처치 주변에서만 그 결과를 해석해야하고, 그 범위를 벗어났을 때에는 우리가 구한 결과값이 다르게 해석되어야 합니다."
+   ]
   },
   {
    "cell_type": "markdown",