dowhy-bd-sep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "universal-harvey",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kang_\\.conda\\envs\\data_science\\lib\\site-packages\\numpy\\_distributor_init.py:32: UserWarning: loaded more than 1 DLL from .libs:\n",
      "C:\\Users\\kang_\\.conda\\envs\\data_science\\lib\\site-packages\\numpy\\.libs\\libopenblas.JPIJNSWNNAN3CE6LLI5FWSPHUT2VXMTH.gfortran-win_amd64.dll\n",
      "C:\\Users\\kang_\\.conda\\envs\\data_science\\lib\\site-packages\\numpy\\.libs\\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll\n",
      "  stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import dowhy\n",
    "import causalml\n",
    "import econml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "agreed-stack",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "insured-confidentiality",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "refined-monte",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = pd.read_csv('combined_ca_bp_new.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "invisible-rapid",
   "metadata": {},
   "outputs": [],
   "source": [
    "bdr = pd.read_csv('biodegradation_15.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "selective-fishing",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = combined[combined['Oil']==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "superior-heaven",
   "metadata": {},
   "outputs": [],
   "source": [
    "bdr = bdr.melt(id_vars=('salinity','dispersant'),value_vars=['1','2','3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "banned-clothing",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    salinity  dispersant variable     value\n",
       "0          0       False        1  0.318678\n",
       "1          0        True        1  0.343368\n",
       "2          5       False        1  0.427612\n",
       "3          5        True        1  0.423018\n",
       "4         15       False        1  0.421063\n",
       "5         15        True        1  0.323758\n",
       "6         35       False        1  0.404759\n",
       "7         35        True        1  0.344526\n",
       "8         50       False        1  0.311191\n",
       "9         50        True        1  0.412678\n",
       "10        70       False        1  0.234204\n",
       "11        70        True        1  0.345556\n",
       "12         0       False        2  0.359220\n",
       "13         0        True        2  0.347308\n",
       "14         5       False        2  0.451578\n",
       "15         5        True        2  0.445144\n",
       "16        15       False        2  0.447101\n",
       "17        15        True        2  0.344883\n",
       "18        35       False        2  0.427440\n",
       "19        35        True        2  0.375033\n",
       "20        50       False        2  0.332558\n",
       "21        50        True        2  0.426855\n",
       "22        70       False        2  0.274071\n",
       "23        70        True        2  0.366519\n",
       "24         0       False        3  0.302926\n",
       "25         0        True        3  0.311070\n",
       "26         5       False        3  0.398788\n",
       "27         5        True        3  0.369848\n",
       "28        15       False        3  0.367579\n",
       "29        15        True        3  0.281153\n",
       "30        35       False        3  0.351774\n",
       "31        35        True        3  0.299044\n",
       "32        50       False        3  0.258206\n",
       "33        50        True        3  0.378364\n",
       "34        70       False        3  0.185795\n",
       "35        70        True        3  0.297523"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "organized-direction",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "collective-bathroom",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['bdr']=bdr['value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "rental-stroke",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.drop(columns=['Oil'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "infinite-recipe",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.drop(columns=['index'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "retained-generator",
   "metadata": {},
   "outputs": [],
   "source": [
    "#combined.to_csv('new_combined.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "split-planner",
   "metadata": {},
   "outputs": [
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       "      <td>0.139</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.447101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.344883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.427440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.375033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.332558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.426855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.274071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.366519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.302926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.311070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.398788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.329</td>\n",
       "      <td>0.369848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.367579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.281153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.351774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.299044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.258206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.378364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.185795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.297523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Salinity_val    Dis  bp_rate  cell_abundance       bdr\n",
       "0              0  False    0.139           0.105  0.318678\n",
       "1              0   True    0.142           0.230  0.343368\n",
       "2              5  False    0.139           0.185  0.427612\n",
       "3              5   True    0.152           0.290  0.423018\n",
       "4             15  False    0.149           0.199  0.421063\n",
       "5             15   True    0.145           0.373  0.323758\n",
       "6             35  False    0.109           0.098  0.404759\n",
       "7             35   True    0.115           0.249  0.344526\n",
       "8             50  False    0.089           0.063  0.311191\n",
       "9             50   True    0.179           0.245  0.412678\n",
       "10            70  False    0.082           0.047  0.234204\n",
       "11            70   True    0.198           0.282  0.345556\n",
       "12             0  False    0.125           0.120  0.359220\n",
       "13             0   True    0.129           0.191  0.347308\n",
       "14             5  False    0.152           0.175  0.451578\n",
       "15             5   True    0.159           0.296  0.445144\n",
       "16            15  False    0.139           0.175  0.447101\n",
       "17            15   True    0.156           0.382  0.344883\n",
       "18            35  False    0.123           0.132  0.427440\n",
       "19            35   True    0.125           0.236  0.375033\n",
       "20            50  False    0.093           0.072  0.332558\n",
       "21            50   True    0.183           0.251  0.426855\n",
       "22            70  False    0.089           0.056  0.274071\n",
       "23            70   True    0.186           0.309  0.366519\n",
       "24             0  False    0.135           0.111  0.302926\n",
       "25             0   True    0.128           0.185  0.311070\n",
       "26             5  False    0.167           0.195  0.398788\n",
       "27             5   True    0.149           0.329  0.369848\n",
       "28            15  False    0.121           0.185  0.367579\n",
       "29            15   True    0.129           0.358  0.281153\n",
       "30            35  False    0.101           0.099  0.351774\n",
       "31            35   True    0.120           0.255  0.299044\n",
       "32            50  False    0.091           0.060  0.258206\n",
       "33            50   True    0.199           0.279  0.378364\n",
       "34            70  False    0.076           0.042  0.185795\n",
       "35            70   True    0.201           0.299  0.297523"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "round-source",
   "metadata": {},
   "outputs": [],
   "source": [
    "causal_graph = \"\"\"digraph {U[label=\"Unobserved Confounders\"];\n",
    "\"Salinity_val\"->\"bdr\";\"Salinity_val\"->\"bp_rate\";\"Salinity_val\"->\"cell_abundance\"\n",
    "\"Dis\"->\"bdr\";\"Dis\"->\"cell_abundance\";\"Dis\"->\"bp_rate\";\n",
    "\"cell_abundance\"->\"bp_rate\";\"cell_abundance\"->\"bdr\";\n",
    "\"bp_rate\"->\"bdr\"\n",
    "}\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "civic-novelty",
   "metadata": {},
   "outputs": [],
   "source": [
    "def causal_estimate(treatment,outcome):\n",
    "    model = dowhy.CausalModel(data = cmbd_trans,\n",
    "                        treatment = treatment,\n",
    "                        outcome = outcome,\n",
    "                        graph = causal_graph.replace(\"\\n\", \" \"))\n",
    "    estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
    "    estimate_li = model.estimate_effect(estimand,method_name = \"backdoor.linear_regression\", method_params = None)\n",
    "    estimate_forest = model.estimate_effect(estimand,method_name =\"backdoor.econml.ortho_forest.ContinuousTreatmentOrthoForest\",method_params = {\"init_params\":{\"n_jobs\":-1},\"fit_params\":{}})\n",
    "    return estimate_li,estimate_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "universal-semiconductor",
   "metadata": {},
   "outputs": [],
   "source": [
    "def li_estimate(treatment,outcome):\n",
    "    model = dowhy.CausalModel(data = cmbd_trans,\n",
    "                        treatment = treatment,\n",
    "                        outcome = outcome,\n",
    "                        graph = causal_graph.replace(\"\\n\", \" \"))\n",
    "    estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
    "    estimate_li = model.estimate_effect(estimand,method_name = \"backdoor.linear_regression\", method_params = None)\n",
    "    return estimate_li"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "acute-canada",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = sklearn.preprocessing.MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "legitimate-seeker",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = scaler.fit_transform(combined.loc[:,'bp_rate':'bdr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "blind-gentleman",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmbd_trans = combined.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "personal-manufacturer",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmbd_trans.loc[:,'bp_rate':'bdr'] = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "banner-energy",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salinity_val</th>\n",
       "      <th>Dis</th>\n",
       "      <th>bp_rate</th>\n",
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       "      <td>5</td>\n",
       "      <td>True</td>\n",
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       "      <td>0.729412</td>\n",
       "      <td>0.892547</td>\n",
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       "      <th>4</th>\n",
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       "      <td>False</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.461765</td>\n",
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       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.973529</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.164706</td>\n",
       "      <td>0.823847</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.608824</td>\n",
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       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
       "      <td>0.104</td>\n",
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       "      <th>9</th>\n",
       "      <td>50</td>\n",
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       "      <td>0.824</td>\n",
       "      <td>0.597059</td>\n",
       "      <td>0.853640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.014706</td>\n",
       "      <td>0.182138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.601095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.229412</td>\n",
       "      <td>0.652507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.438235</td>\n",
       "      <td>0.607688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.391176</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.747059</td>\n",
       "      <td>0.975794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.391176</td>\n",
       "      <td>0.983157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.598565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.909184</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.570588</td>\n",
       "      <td>0.712005</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>0.552191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>0.856</td>\n",
       "      <td>0.614706</td>\n",
       "      <td>0.906983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.041176</td>\n",
       "      <td>0.332136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.785294</td>\n",
       "      <td>0.679969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.202941</td>\n",
       "      <td>0.440704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.416</td>\n",
       "      <td>0.420588</td>\n",
       "      <td>0.471345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.801382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.844118</td>\n",
       "      <td>0.692495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.420588</td>\n",
       "      <td>0.683960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.929412</td>\n",
       "      <td>0.358782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.167647</td>\n",
       "      <td>0.624490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.626471</td>\n",
       "      <td>0.426097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.052941</td>\n",
       "      <td>0.272446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.697059</td>\n",
       "      <td>0.724535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.755882</td>\n",
       "      <td>0.420374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Salinity_val    Dis  bp_rate  cell_abundance       bdr\n",
       "0              0  False    0.504        0.185294  0.499968\n",
       "1              0   True    0.528        0.552941  0.592865\n",
       "2              5  False    0.504        0.420588  0.909829\n",
       "3              5   True    0.608        0.729412  0.892547\n",
       "4             15  False    0.584        0.461765  0.885189\n",
       "5             15   True    0.552        0.973529  0.519082\n",
       "6             35  False    0.264        0.164706  0.823847\n",
       "7             35   True    0.312        0.608824  0.597221\n",
       "8             50  False    0.104        0.061765  0.471801\n",
       "9             50   True    0.824        0.597059  0.853640\n",
       "10            70  False    0.048        0.014706  0.182138\n",
       "11            70   True    0.976        0.705882  0.601095\n",
       "12             0  False    0.392        0.229412  0.652507\n",
       "13             0   True    0.424        0.438235  0.607688\n",
       "14             5  False    0.608        0.391176  1.000000\n",
       "15             5   True    0.664        0.747059  0.975794\n",
       "16            15  False    0.504        0.391176  0.983157\n",
       "17            15   True    0.640        1.000000  0.598565\n",
       "18            35  False    0.376        0.264706  0.909184\n",
       "19            35   True    0.392        0.570588  0.712005\n",
       "20            50  False    0.136        0.088235  0.552191\n",
       "21            50   True    0.856        0.614706  0.906983\n",
       "22            70  False    0.104        0.041176  0.332136\n",
       "23            70   True    0.880        0.785294  0.679969\n",
       "24             0  False    0.472        0.202941  0.440704\n",
       "25             0   True    0.416        0.420588  0.471345\n",
       "26             5  False    0.728        0.450000  0.801382\n",
       "27             5   True    0.584        0.844118  0.692495\n",
       "28            15  False    0.360        0.420588  0.683960\n",
       "29            15   True    0.424        0.929412  0.358782\n",
       "30            35  False    0.200        0.167647  0.624490\n",
       "31            35   True    0.352        0.626471  0.426097\n",
       "32            50  False    0.120        0.052941  0.272446\n",
       "33            50   True    0.984        0.697059  0.724535\n",
       "34            70  False    0.000        0.000000  0.000000\n",
       "35            70   True    1.000        0.755882  0.420374"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmbd_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "empirical-bacon",
   "metadata": {},
   "outputs": [],
   "source": [
    "param = [(\"Dis\",\"bdr\"),(\"Dis\",\"cell_abundance\"),(\"Dis\",\"bp_rate\"),(\"Salinity_val\",\"bdr\"),(\"Salinity_val\",\"bp_rate\"),(\"Salinity_val\",\"cell_abundance\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dressed-oakland",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_ca = [(\"cell_abundance\",\"bdr\"),(\"cell_abundance\",\"bp_rate\"),(\"bp_rate\",\"bdr\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "revolutionary-highway",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['Dis'] on outcome ['bdr']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_identifier:Frontdoor variables for treatment and outcome:['bp_rate']\n",
      "INFO:dowhy.causal_identifier:All common causes are observed. Causal effect can be identified.\n",
      "INFO:dowhy.causal_identifier:All common causes are observed. Causal effect can be identified.\n",
      "INFO:dowhy.causal_estimator:b: bdr~Dis+Salinity_val+Dis*Salinity_val\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "C:\\Users\\kang_\\.conda\\envs\\data_science\\lib\\site-packages\\dowhy\\causal_estimators\\econml.py:62: FutureWarning: The ContinuousTreatmentOrthoForest class has been renamed to DMLOrthoForest; an upcoming release will remove support for the old name\n",
      "  self.estimator = estimator_class(**self.method_params[\"init_params\"])\n",
      "INFO:dowhy.causal_estimator:b: bdr~Dis+Salinity_val | Salinity_val\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  93 tasks      | elapsed:    5.3s\n",
      "[Parallel(n_jobs=-1)]: Done 461 out of 500 | elapsed:    5.7s remaining:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    7.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 110 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done 461 out of 500 | elapsed:    0.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  36 | elapsed:    5.5s remaining:   14.3s\n",
      "[Parallel(n_jobs=-1)]: Done  23 out of  36 | elapsed:    6.0s remaining:    3.3s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.6s finished\n",
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['Dis'] on outcome ['cell_abundance']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_identifier:Frontdoor variables for treatment and outcome:['bp_rate']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:All common causes are observed. Causal effect can be identified.\n",
      "INFO:dowhy.causal_estimator:b: cell_abundance~Dis+Salinity_val+Dis*Salinity_val\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "C:\\Users\\kang_\\.conda\\envs\\data_science\\lib\\site-packages\\dowhy\\causal_estimators\\econml.py:62: FutureWarning: The ContinuousTreatmentOrthoForest class has been renamed to DMLOrthoForest; an upcoming release will remove support for the old name\n",
      "  self.estimator = estimator_class(**self.method_params[\"init_params\"])\n",
      "INFO:dowhy.causal_estimator:b: cell_abundance~Dis+Salinity_val | Salinity_val\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  92 tasks      | elapsed:    5.4s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    7.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 156 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  36 | elapsed:    5.3s remaining:   13.9s\n",
      "[Parallel(n_jobs=-1)]: Done  23 out of  36 | elapsed:    7.5s remaining:    4.2s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.9s finished\n",
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['Dis'] on outcome ['bp_rate']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_identifier:Frontdoor variables for treatment and outcome:['cell_abundance']\n",
      "INFO:dowhy.causal_identifier:All common causes are observed. Causal effect can be identified.\n",
      "INFO:dowhy.causal_identifier:All common causes are observed. Causal effect can be identified.\n",
      "INFO:dowhy.causal_estimator:b: bp_rate~Dis+Salinity_val+Dis*Salinity_val\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "C:\\Users\\kang_\\.conda\\envs\\data_science\\lib\\site-packages\\dowhy\\causal_estimators\\econml.py:62: FutureWarning: The ContinuousTreatmentOrthoForest class has been renamed to DMLOrthoForest; an upcoming release will remove support for the old name\n",
      "  self.estimator = estimator_class(**self.method_params[\"init_params\"])\n",
      "INFO:dowhy.causal_estimator:b: bp_rate~Dis+Salinity_val | Salinity_val\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 112 tasks      | elapsed:    5.2s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    7.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  36 | elapsed:    5.3s remaining:   13.9s\n",
      "[Parallel(n_jobs=-1)]: Done  23 out of  36 | elapsed:    6.1s remaining:    3.4s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.6s finished\n",
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['Salinity_val'] on outcome ['bdr']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_identifier:Frontdoor variables for treatment and outcome:['bp_rate']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_identifier:All common causes are observed. Causal effect can be identified.\n",
      "INFO:dowhy.causal_identifier:All common causes are observed. Causal effect can be identified.\n",
      "INFO:dowhy.causal_estimator:b: bdr~Salinity_val+Dis+Salinity_val*Dis\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "C:\\Users\\kang_\\.conda\\envs\\data_science\\lib\\site-packages\\dowhy\\causal_estimators\\econml.py:62: FutureWarning: The ContinuousTreatmentOrthoForest class has been renamed to DMLOrthoForest; an upcoming release will remove support for the old name\n",
      "  self.estimator = estimator_class(**self.method_params[\"init_params\"])\n",
      "INFO:dowhy.causal_estimator:b: bdr~Salinity_val+Dis | Dis\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 113 tasks      | elapsed:    5.2s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    7.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  36 | elapsed:    5.3s remaining:   14.0s\n",
      "[Parallel(n_jobs=-1)]: Done  23 out of  36 | elapsed:    6.8s remaining:    3.8s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.6s finished\n",
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['Salinity_val'] on outcome ['bp_rate']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_identifier:Frontdoor variables for treatment and outcome:['cell_abundance']\n",
      "INFO:dowhy.causal_identifier:All common causes are observed. Causal effect can be identified.\n",
      "INFO:dowhy.causal_identifier:All common causes are observed. Causal effect can be identified.\n",
      "INFO:dowhy.causal_estimator:b: bp_rate~Salinity_val+Dis+Salinity_val*Dis\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "C:\\Users\\kang_\\.conda\\envs\\data_science\\lib\\site-packages\\dowhy\\causal_estimators\\econml.py:62: FutureWarning: The ContinuousTreatmentOrthoForest class has been renamed to DMLOrthoForest; an upcoming release will remove support for the old name\n",
      "  self.estimator = estimator_class(**self.method_params[\"init_params\"])\n",
      "INFO:dowhy.causal_estimator:b: bp_rate~Salinity_val+Dis | Dis\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 112 tasks      | elapsed:    5.1s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    7.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 164 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  36 | elapsed:    5.5s remaining:   14.6s\n",
      "[Parallel(n_jobs=-1)]: Done  23 out of  36 | elapsed:    6.2s remaining:    3.4s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.7s finished\n",
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['Salinity_val'] on outcome ['cell_abundance']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_identifier:Frontdoor variables for treatment and outcome:['bp_rate']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:All common causes are observed. Causal effect can be identified.\n",
      "INFO:dowhy.causal_estimator:b: cell_abundance~Salinity_val+Dis+Salinity_val*Dis\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "C:\\Users\\kang_\\.conda\\envs\\data_science\\lib\\site-packages\\dowhy\\causal_estimators\\econml.py:62: FutureWarning: The ContinuousTreatmentOrthoForest class has been renamed to DMLOrthoForest; an upcoming release will remove support for the old name\n",
      "  self.estimator = estimator_class(**self.method_params[\"init_params\"])\n",
      "INFO:dowhy.causal_estimator:b: cell_abundance~Salinity_val+Dis | Dis\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  91 tasks      | elapsed:    5.6s\n",
      "[Parallel(n_jobs=-1)]: Done 461 out of 500 | elapsed:    5.9s remaining:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    7.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 176 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done 500 out of 500 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 20 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  36 | elapsed:    5.3s remaining:   14.0s\n",
      "[Parallel(n_jobs=-1)]: Done  23 out of  36 | elapsed:    6.5s remaining:    3.6s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    7.7s finished\n"
     ]
    }
   ],
   "source": [
    "result_1=[]\n",
    "for i in param:\n",
    "        result_1.append(causal_estimate(*i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "local-enemy",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['cell_abundance'] on outcome ['bdr']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_identifier:Frontdoor variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_estimator:b: bdr~cell_abundance+Dis+Salinity_val\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['cell_abundance'] on outcome ['bp_rate']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_identifier:Frontdoor variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_estimator:b: bp_rate~cell_abundance+Dis+Salinity_val\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['bp_rate'] on outcome ['bdr']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_identifier:Frontdoor variables for treatment and outcome:[]\n",
      "INFO:dowhy.causal_estimator:b: bdr~bp_rate+Dis+cell_abundance+Salinity_val\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n"
     ]
    }
   ],
   "source": [
    "result_2=[]\n",
    "for i in param_ca:\n",
    "        result_2.append(li_estimate(*i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "excited-archive",
   "metadata": {},
   "outputs": [],
   "source": [
    "param = [(\"Dis\",\"bdr\"),(\"Dis\",\"cell_abundance\"),(\"Dis\",\"bp_rate\"),(\"Salinity_val\",\"bdr\"),(\"Salinity_val\",\"bp_rate\"),(\"Salinity_val\",\"cell_abundance\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "proud-condition",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_ca = [(\"cell_abundance\",\"bdr\"),(\"cell_abundance\",\"bp_rate\"),(\"bp_rate\",\"bdr\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "adjacent-disclosure",
   "metadata": {},
   "outputs": [],
   "source": [
    "cate_li = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "vocational-reason",
   "metadata": {},
   "outputs": [],
   "source": [
    "cate_li['dis->bdr'] = result_1[0][0].conditional_estimates.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "narrative-steering",
   "metadata": {},
   "outputs": [],
   "source": [
    "cate_li['dis->ca'] = result_1[1][0].conditional_estimates.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "decreased-nightmare",
   "metadata": {},
   "outputs": [],
   "source": [
    "cate_li['dis->bp_rate'] = result_1[2][0].conditional_estimates.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "organic-commander",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_li = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "productive-imaging",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_li['dis->bdr'] = [result_1[0][0].value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "clear-suspension",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_li['dis->ca'] = [result_1[1][0].value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "optional-harrison",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_li['dis->bp_rate'] = [result_1[2][0].value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "exciting-religious",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_li['sal->bdr'] = [result_1[3][0].value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "motivated-empty",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_li['sal->bp_rate'] = [result_1[4][0].value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "accompanied-tonight",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_li['sal->ca'] = [result_1[5][0].value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "vertical-alert",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_li['ca->bdr'] = [result_2[0].value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "introductory-language",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_li['ca->bp_rate'] = [result_2[1].value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "extended-owner",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_li['bp_rate->bdr'] = [result_2[2].value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "danish-anxiety",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   dis->bdr   dis->ca  dis->bp_rate  sal->bdr  sal->bp_rate   sal->ca  \\\n",
       "0  0.033675  0.477124      0.300444 -0.003827     -0.000691 -0.002337   \n",
       "\n",
       "    ca->bdr  ca->bp_rate  bp_rate->bdr  \n",
       "0  0.525198     0.812709      0.544232  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_li"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "recorded-philosophy",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "juvenile-metadata",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml['dis->bdr'] = result_1[0][1].cate_estimates\n",
    "df_ml['dis->ca'] = result_1[1][1].cate_estimates\n",
    "df_ml['dis->bp_rate'] = result_1[2][1].cate_estimates\n",
    "df_ml['sal->bdr'] = result_1[3][1].cate_estimates\n",
    "df_ml['sal->bp_rate'] = result_1[0][1].cate_estimates\n",
    "df_ml['sal->ca'] = result_1[1][1].cate_estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "adverse-commercial",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ml['Salinity_val']=combined['Salinity_val']\n",
    "df_ml['Dis']=combined['Dis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "frozen-rider",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.051162</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>-0.203957</td>\n",
       "      <td>0.313013</td>\n",
       "      <td>5</td>\n",
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       "      <td>-0.203957</td>\n",
       "      <td>0.313005</td>\n",
       "      <td>-0.051162</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>-0.203957</td>\n",
       "      <td>0.313005</td>\n",
       "      <td>5</td>\n",
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       "      <td>0.395063</td>\n",
       "      <td>0.092038</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>-0.124659</td>\n",
       "      <td>0.395063</td>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.395063</td>\n",
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       "      <td>0.000157</td>\n",
       "      <td>-0.124659</td>\n",
       "      <td>0.395063</td>\n",
       "      <td>15</td>\n",
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       "      <td>0.516633</td>\n",
       "      <td>0.378437</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>0.033938</td>\n",
       "      <td>0.516633</td>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.033938</td>\n",
       "      <td>0.493624</td>\n",
       "      <td>0.378437</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.033938</td>\n",
       "      <td>0.493624</td>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.608061</td>\n",
       "      <td>0.593237</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.608061</td>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.604228</td>\n",
       "      <td>0.593237</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.604228</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.729773</td>\n",
       "      <td>0.879636</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.729773</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.729773</td>\n",
       "      <td>0.879636</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.729773</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>-0.243607</td>\n",
       "      <td>0.305428</td>\n",
       "      <td>-0.122761</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>-0.243607</td>\n",
       "      <td>0.305428</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.243607</td>\n",
       "      <td>0.303779</td>\n",
       "      <td>-0.122761</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>-0.243607</td>\n",
       "      <td>0.303779</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.203957</td>\n",
       "      <td>0.334207</td>\n",
       "      <td>-0.051162</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>-0.203957</td>\n",
       "      <td>0.334207</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.203957</td>\n",
       "      <td>0.340718</td>\n",
       "      <td>-0.051162</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>-0.203957</td>\n",
       "      <td>0.340718</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.124659</td>\n",
       "      <td>0.377548</td>\n",
       "      <td>0.092038</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>-0.124659</td>\n",
       "      <td>0.377548</td>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.124659</td>\n",
       "      <td>0.395063</td>\n",
       "      <td>0.092038</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>-0.124659</td>\n",
       "      <td>0.395063</td>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.033938</td>\n",
       "      <td>0.516776</td>\n",
       "      <td>0.378437</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>0.033938</td>\n",
       "      <td>0.516776</td>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.033938</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>0.378437</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.033938</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.608061</td>\n",
       "      <td>0.593237</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.608061</td>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.608061</td>\n",
       "      <td>0.593237</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.608061</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.692588</td>\n",
       "      <td>0.879636</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.692588</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.729773</td>\n",
       "      <td>0.879636</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.729773</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.243607</td>\n",
       "      <td>0.303779</td>\n",
       "      <td>-0.122761</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>-0.243607</td>\n",
       "      <td>0.303779</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.243607</td>\n",
       "      <td>0.303779</td>\n",
       "      <td>-0.122761</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>-0.243607</td>\n",
       "      <td>0.303779</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.203957</td>\n",
       "      <td>0.334207</td>\n",
       "      <td>-0.051162</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>-0.203957</td>\n",
       "      <td>0.334207</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.203957</td>\n",
       "      <td>0.313013</td>\n",
       "      <td>-0.051162</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>-0.203957</td>\n",
       "      <td>0.313013</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.124659</td>\n",
       "      <td>0.395063</td>\n",
       "      <td>0.092038</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>-0.124659</td>\n",
       "      <td>0.395063</td>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.124659</td>\n",
       "      <td>0.395063</td>\n",
       "      <td>0.092038</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>-0.124659</td>\n",
       "      <td>0.395063</td>\n",
       "      <td>15</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.033938</td>\n",
       "      <td>0.516776</td>\n",
       "      <td>0.378437</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>0.033938</td>\n",
       "      <td>0.516776</td>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.033938</td>\n",
       "      <td>0.516776</td>\n",
       "      <td>0.378437</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.033938</td>\n",
       "      <td>0.516776</td>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.608061</td>\n",
       "      <td>0.593237</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.608061</td>\n",
       "      <td>50</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.608061</td>\n",
       "      <td>0.593237</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.152886</td>\n",
       "      <td>0.608061</td>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.729773</td>\n",
       "      <td>0.879636</td>\n",
       "      <td>-0.007960</td>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.729773</td>\n",
       "      <td>70</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.729773</td>\n",
       "      <td>0.879636</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.311483</td>\n",
       "      <td>0.729773</td>\n",
       "      <td>70</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dis->bdr   dis->ca  dis->bp_rate  sal->bdr  sal->bp_rate   sal->ca  \\\n",
       "0  -0.243607  0.303779     -0.125582 -0.007960     -0.243607  0.303779   \n",
       "1  -0.243607  0.270385     -0.122761  0.000157     -0.243607  0.270385   \n",
       "2  -0.203957  0.313013     -0.051162 -0.007960     -0.203957  0.313013   \n",
       "3  -0.203957  0.313005     -0.051162  0.000157     -0.203957  0.313005   \n",
       "4  -0.124659  0.395063      0.092038 -0.007960     -0.124659  0.395063   \n",
       "5  -0.124659  0.395063      0.092038  0.000157     -0.124659  0.395063   \n",
       "6   0.033938  0.516633      0.378437 -0.007960      0.033938  0.516633   \n",
       "7   0.033938  0.493624      0.378437  0.000157      0.033938  0.493624   \n",
       "8   0.152886  0.608061      0.593237 -0.007960      0.152886  0.608061   \n",
       "9   0.152886  0.604228      0.593237  0.000157      0.152886  0.604228   \n",
       "10  0.311483  0.729773      0.879636 -0.007960      0.311483  0.729773   \n",
       "11  0.311483  0.729773      0.879636  0.000157      0.311483  0.729773   \n",
       "12 -0.243607  0.305428     -0.122761 -0.007960     -0.243607  0.305428   \n",
       "13 -0.243607  0.303779     -0.122761  0.000157     -0.243607  0.303779   \n",
       "14 -0.203957  0.334207     -0.051162 -0.007960     -0.203957  0.334207   \n",
       "15 -0.203957  0.340718     -0.051162  0.000157     -0.203957  0.340718   \n",
       "16 -0.124659  0.377548      0.092038 -0.007960     -0.124659  0.377548   \n",
       "17 -0.124659  0.395063      0.092038  0.000157     -0.124659  0.395063   \n",
       "18  0.033938  0.516776      0.378437 -0.007960      0.033938  0.516776   \n",
       "19  0.033938  0.506618      0.378437  0.000157      0.033938  0.506618   \n",
       "20  0.152886  0.608061      0.593237 -0.007960      0.152886  0.608061   \n",
       "21  0.152886  0.608061      0.593237  0.000157      0.152886  0.608061   \n",
       "22  0.311483  0.692588      0.879636 -0.007960      0.311483  0.692588   \n",
       "23  0.311483  0.729773      0.879636  0.000228      0.311483  0.729773   \n",
       "24 -0.243607  0.303779     -0.122761 -0.007960     -0.243607  0.303779   \n",
       "25 -0.243607  0.303779     -0.122761  0.000157     -0.243607  0.303779   \n",
       "26 -0.203957  0.334207     -0.051162 -0.007960     -0.203957  0.334207   \n",
       "27 -0.203957  0.313013     -0.051162  0.000157     -0.203957  0.313013   \n",
       "28 -0.124659  0.395063      0.092038 -0.007960     -0.124659  0.395063   \n",
       "29 -0.124659  0.395063      0.092038  0.000157     -0.124659  0.395063   \n",
       "30  0.033938  0.516776      0.378437 -0.007960      0.033938  0.516776   \n",
       "31  0.033938  0.516776      0.378437  0.000157      0.033938  0.516776   \n",
       "32  0.152886  0.608061      0.593237 -0.007960      0.152886  0.608061   \n",
       "33  0.152886  0.608061      0.593237  0.000157      0.152886  0.608061   \n",
       "34  0.311483  0.729773      0.879636 -0.007960      0.311483  0.729773   \n",
       "35  0.311483  0.729773      0.879636  0.000157      0.311483  0.729773   \n",
       "\n",
       "    Salinity_val    Dis  \n",
       "0              0  False  \n",
       "1              0   True  \n",
       "2              5  False  \n",
       "3              5   True  \n",
       "4             15  False  \n",
       "5             15   True  \n",
       "6             35  False  \n",
       "7             35   True  \n",
       "8             50  False  \n",
       "9             50   True  \n",
       "10            70  False  \n",
       "11            70   True  \n",
       "12             0  False  \n",
       "13             0   True  \n",
       "14             5  False  \n",
       "15             5   True  \n",
       "16            15  False  \n",
       "17            15   True  \n",
       "18            35  False  \n",
       "19            35   True  \n",
       "20            50  False  \n",
       "21            50   True  \n",
       "22            70  False  \n",
       "23            70   True  \n",
       "24             0  False  \n",
       "25             0   True  \n",
       "26             5  False  \n",
       "27             5   True  \n",
       "28            15  False  \n",
       "29            15   True  \n",
       "30            35  False  \n",
       "31            35   True  \n",
       "32            50  False  \n",
       "33            50   True  \n",
       "34            70  False  \n",
       "35            70   True  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "resistant-missile",
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_avg = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "mechanical-bouquet",
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_avg ['dis->bdr'] = [result_1[0][1].value]\n",
    "ml_avg ['dis->ca'] = [result_1[1][1].value]\n",
    "ml_avg ['dis->bp_rate'] = [result_1[2][1].value]\n",
    "ml_avg ['sal->bdr'] = [result_1[3][1].value]\n",
    "ml_avg ['sal->bp_rate'] = [result_1[0][1].value]\n",
    "ml_avg ['sal->ca'] = [result_1[1][1].value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "controversial-length",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dis-&gt;bdr</th>\n",
       "      <th>dis-&gt;ca</th>\n",
       "      <th>dis-&gt;bp_rate</th>\n",
       "      <th>sal-&gt;bdr</th>\n",
       "      <th>sal-&gt;bp_rate</th>\n",
       "      <th>sal-&gt;ca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.012319</td>\n",
       "      <td>0.476254</td>\n",
       "      <td>0.294826</td>\n",
       "      <td>-0.0039</td>\n",
       "      <td>-0.012319</td>\n",
       "      <td>0.476254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dis->bdr   dis->ca  dis->bp_rate  sal->bdr  sal->bp_rate   sal->ca\n",
       "0 -0.012319  0.476254      0.294826   -0.0039     -0.012319  0.476254"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_avg"
   ]
  }
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