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akelleh/weighting_do_sampler.ipynb

Created February 17, 2019 04:23
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weighting_do_sampler.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import dowhy.datasets\n",
"from dowhy.do_samplers.weighting_sampler import WeightingSampler\n",
"from dowhy.do_why import CausalModel\n",
"from dowhy.api.causal_data_frame import CausalDataFrame\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from statsmodels.api import OLS"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"\n",
"data = dowhy.datasets.linear_dataset(beta=5,\n",
" num_common_causes=1,\n",
" num_instruments = 0,\n",
" num_samples=1000,\n",
" treatment_is_binary=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"data['dot_graph'] = 'digraph { v ->y;X0-> v;X0-> y;}'\n",
"df = data['df']\n",
"df['y'] = df['y'] + np.random.normal(size=len(df)) # without noise, the variance in Y|X, Z is zero, and mcmc fails.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:dowhy.do_why:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n",
"INFO:dowhy.causal_identifier:Common causes of treatment and outcome:{'X0', 'U'}\n",
"INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
"INFO:dowhy.do_sampler:Using WeightingSampler for do sampling.\n",
"INFO:dowhy.do_sampler:Caution: do samplers assume iid data.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['X0']\n",
"yes\n",
"{'observed': 'yes'}\n",
"Model to find the causal effect of treatment v on outcome y\n",
"{'observed': 'yes'}\n",
"{'label': 'Unobserved Confounders', 'observed': 'no'}\n",
"All common causes are observed. Causal effect can be identified.\n",
"WeightingSampler\n",
"treatments ['v']\n",
"backdoor ['X0']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f42764a8320>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cdf = CausalDataFrame(df)\n",
"cdf.causal.do(x={'v': 1}, \n",
" variable_types={'v': 'b', 'y': 'c', 'X0': 'c'}, \n",
" outcome='y',\n",
" method='weighting', \n",
" common_causes=['X0'],\n",
" keep_original_treatment=True,\n",
" proceed_when_unidentifiable=True).groupby('v').mean().plot(y='y', kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"cdf = CausalDataFrame(df)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_identifier:Common causes of treatment and outcome:{'X0', 'U'}\n",
"INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
"INFO:dowhy.do_sampler:Using WeightingSampler for do sampling.\n",
"INFO:dowhy.do_sampler:Caution: do samplers assume iid data.\n",
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n",
"INFO:dowhy.causal_identifier:Common causes of treatment and outcome:{'X0', 'U'}\n",
"INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error: Pygraphviz cannot be loaded. No module named 'pygraphviz'\n",
"Trying pydot ...\n",
"['X0']\n",
"yes\n",
"{'observed': 'yes'}\n",
"Model to find the causal effect of treatment v on outcome y\n",
"{'observed': 'yes'}\n",
"{'label': 'Unobserved Confounders', 'observed': 'no'}\n",
"All common causes are observed. Causal effect can be identified.\n",
"WeightingSampler\n",
"treatments ['v']\n",
"backdoor ['X0']\n",
"{'observed': 'yes'}\n",
"{'label': 'Unobserved Confounders', 'observed': 'no'}\n",
"All common causes are observed. Causal effect can be identified.\n",
"WeightingSampler\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
}
],
"source": [
"cdf_1 = cdf.causal.do(x={'v': 1}, \n",
" variable_types={'v': 'b', 'y': 'c', 'X0': 'c'}, \n",
" outcome='y',\n",
" method='weighting', \n",
" dot_graph=data['dot_graph'],\n",
" proceed_when_unidentifiable=True)\n",
"\n",
"cdf_0 = cdf.causal.do(x={'v': 0}, \n",
" variable_types={'v': 'b', 'y': 'c', 'X0': 'c'}, \n",
" outcome='y',\n",
" method='weighting', \n",
" dot_graph=data['dot_graph'],\n",
" proceed_when_unidentifiable=True,\n",
" use_previous_sampler=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <td>0.670913</td>\n",
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" <td>0.717521</td>\n",
" <td>3.540091</td>\n",
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" <td>0.235394</td>\n",
" <td>1.307863</td>\n",
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" <td>0.612730</td>\n",
" <td>0.0</td>\n",
" <td>0.101522</td>\n",
" <td>0.763345</td>\n",
" <td>4.225564</td>\n",
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" <td>0.0</td>\n",
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" <td>0.472742</td>\n",
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" <td>0.0</td>\n",
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" <td>0.800005</td>\n",
" <td>5.000115</td>\n",
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" <td>0.448100</td>\n",
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" <td>2.359522</td>\n",
" <td>0.714566</td>\n",
" <td>3.503436</td>\n",
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" <tr>\n",
" <th>20</th>\n",
" <td>-1.378189</td>\n",
" <td>0.0</td>\n",
" <td>-2.404268</td>\n",
" <td>0.130805</td>\n",
" <td>1.150490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.367733</td>\n",
" <td>0.0</td>\n",
" <td>-1.362551</td>\n",
" <td>0.688677</td>\n",
" <td>3.212097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-0.677443</td>\n",
" <td>0.0</td>\n",
" <td>1.005151</td>\n",
" <td>0.306809</td>\n",
" <td>1.442605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>-1.074204</td>\n",
" <td>0.0</td>\n",
" <td>-0.822769</td>\n",
" <td>0.193741</td>\n",
" <td>1.240297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>-0.728784</td>\n",
" <td>0.0</td>\n",
" <td>0.335534</td>\n",
" <td>0.290261</td>\n",
" <td>1.408969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>-0.174916</td>\n",
" <td>0.0</td>\n",
" <td>-0.816614</td>\n",
" <td>0.489638</td>\n",
" <td>1.959395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.131783</td>\n",
" <td>0.0</td>\n",
" <td>-0.348114</td>\n",
" <td>0.606042</td>\n",
" <td>2.538339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.407820</td>\n",
" <td>0.0</td>\n",
" <td>0.111378</td>\n",
" <td>0.701752</td>\n",
" <td>3.352911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>-1.384935</td>\n",
" <td>0.0</td>\n",
" <td>-3.563577</td>\n",
" <td>0.129629</td>\n",
" <td>1.148935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-2.530571</td>\n",
" <td>0.0</td>\n",
" <td>-2.978433</td>\n",
" <td>0.024894</td>\n",
" <td>1.025530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>970</th>\n",
" <td>-0.955608</td>\n",
" <td>0.0</td>\n",
" <td>-3.682694</td>\n",
" <td>0.223861</td>\n",
" <td>1.288429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>971</th>\n",
" <td>-2.717983</td>\n",
" <td>0.0</td>\n",
" <td>-4.138503</td>\n",
" <td>0.018772</td>\n",
" <td>1.019131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>972</th>\n",
" <td>-0.107787</td>\n",
" <td>0.0</td>\n",
" <td>0.199005</td>\n",
" <td>0.515468</td>\n",
" <td>2.063847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>973</th>\n",
" <td>-1.650197</td>\n",
" <td>0.0</td>\n",
" <td>-1.115839</td>\n",
" <td>0.090084</td>\n",
" <td>1.099003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>974</th>\n",
" <td>-1.648183</td>\n",
" <td>0.0</td>\n",
" <td>-1.432377</td>\n",
" <td>0.090339</td>\n",
" <td>1.099310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>975</th>\n",
" <td>0.288879</td>\n",
" <td>0.0</td>\n",
" <td>2.644426</td>\n",
" <td>0.662073</td>\n",
" <td>2.959223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>976</th>\n",
" <td>-0.657748</td>\n",
" <td>0.0</td>\n",
" <td>0.878296</td>\n",
" <td>0.313295</td>\n",
" <td>1.456230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>977</th>\n",
" <td>-0.186507</td>\n",
" <td>0.0</td>\n",
" <td>0.739407</td>\n",
" <td>0.485180</td>\n",
" <td>1.942428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>978</th>\n",
" <td>-0.088590</td>\n",
" <td>0.0</td>\n",
" <td>-0.597798</td>\n",
" <td>0.522845</td>\n",
" <td>2.095755</td>\n",
" </tr>\n",
" <tr>\n",
" <th>979</th>\n",
" <td>-1.200004</td>\n",
" <td>0.0</td>\n",
" <td>0.342928</td>\n",
" <td>0.165267</td>\n",
" <td>1.197989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>980</th>\n",
" <td>0.520743</td>\n",
" <td>0.0</td>\n",
" <td>-0.433905</td>\n",
" <td>0.736818</td>\n",
" <td>3.799653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>981</th>\n",
" <td>0.536033</td>\n",
" <td>0.0</td>\n",
" <td>0.475667</td>\n",
" <td>0.741357</td>\n",
" <td>3.866334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>982</th>\n",
" <td>-0.031386</td>\n",
" <td>0.0</td>\n",
" <td>0.975553</td>\n",
" <td>0.544757</td>\n",
" <td>2.196629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>983</th>\n",
" <td>0.700180</td>\n",
" <td>0.0</td>\n",
" <td>0.390546</td>\n",
" <td>0.786798</td>\n",
" <td>4.690394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>984</th>\n",
" <td>0.353783</td>\n",
" <td>0.0</td>\n",
" <td>0.587299</td>\n",
" <td>0.684054</td>\n",
" <td>3.165097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>985</th>\n",
" <td>0.592212</td>\n",
" <td>0.0</td>\n",
" <td>0.788882</td>\n",
" <td>0.757592</td>\n",
" <td>4.125268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>986</th>\n",
" <td>0.317355</td>\n",
" <td>0.0</td>\n",
" <td>-1.466783</td>\n",
" <td>0.671811</td>\n",
" <td>3.047021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>987</th>\n",
" <td>0.281453</td>\n",
" <td>0.0</td>\n",
" <td>1.043265</td>\n",
" <td>0.659511</td>\n",
" <td>2.936951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>988</th>\n",
" <td>-1.740564</td>\n",
" <td>0.0</td>\n",
" <td>-2.890607</td>\n",
" <td>0.079313</td>\n",
" <td>1.086145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>989</th>\n",
" <td>-0.751195</td>\n",
" <td>0.0</td>\n",
" <td>-0.083296</td>\n",
" <td>0.283205</td>\n",
" <td>1.395100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>990</th>\n",
" <td>-1.057706</td>\n",
" <td>0.0</td>\n",
" <td>-0.308057</td>\n",
" <td>0.197740</td>\n",
" <td>1.246478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>991</th>\n",
" <td>-0.553931</td>\n",
" <td>0.0</td>\n",
" <td>-1.044103</td>\n",
" <td>0.348660</td>\n",
" <td>1.535296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>992</th>\n",
" <td>0.192048</td>\n",
" <td>0.0</td>\n",
" <td>-1.286848</td>\n",
" <td>0.627961</td>\n",
" <td>2.687891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>993</th>\n",
" <td>-1.118731</td>\n",
" <td>0.0</td>\n",
" <td>-1.095691</td>\n",
" <td>0.183258</td>\n",
" <td>1.224377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>994</th>\n",
" <td>-0.917039</td>\n",
" <td>0.0</td>\n",
" <td>-0.857244</td>\n",
" <td>0.234346</td>\n",
" <td>1.306074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>-2.030707</td>\n",
" <td>0.0</td>\n",
" <td>-2.866590</td>\n",
" <td>0.052233</td>\n",
" <td>1.055112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>0.181139</td>\n",
" <td>0.0</td>\n",
" <td>0.942937</td>\n",
" <td>0.624029</td>\n",
" <td>2.659781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>0.125104</td>\n",
" <td>0.0</td>\n",
" <td>-0.041996</td>\n",
" <td>0.603584</td>\n",
" <td>2.522603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>-0.720169</td>\n",
" <td>0.0</td>\n",
" <td>-0.553785</td>\n",
" <td>0.293001</td>\n",
" <td>1.414429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>0.081376</td>\n",
" <td>0.0</td>\n",
" <td>0.596130</td>\n",
" <td>0.587370</td>\n",
" <td>2.423478</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" X0 v y propensity_score weight\n",
"0 0.131783 0.0 -0.348114 0.606042 2.538339\n",
"1 -0.428817 0.0 1.576623 0.393571 1.648998\n",
"2 -1.057706 0.0 -0.308057 0.197740 1.246478\n",
"3 -0.697312 0.0 -1.204937 0.300343 1.429271\n",
"4 -1.028891 0.0 -0.624991 0.204871 1.257658\n",
"5 0.314712 0.0 1.461244 0.670913 3.038709\n",
"6 -0.072427 0.0 -2.179205 0.529049 2.123363\n",
"7 0.457542 0.0 -0.033124 0.717521 3.540091\n",
"8 -2.380950 0.0 -1.673362 0.031141 1.032142\n",
"9 -0.473554 0.0 -1.721827 0.377259 1.605805\n",
"10 0.413320 0.0 -0.438323 0.703521 3.372919\n",
"11 -0.913252 0.0 -1.172680 0.235394 1.307863\n",
"12 0.612730 0.0 0.101522 0.763345 4.225564\n",
"13 -0.430925 0.0 -1.901817 0.392797 1.646896\n",
"14 -0.218885 0.0 -0.803547 0.472742 1.896604\n",
"15 -0.174916 0.0 -0.816614 0.489638 1.959395\n",
"16 -0.305455 0.0 -0.251362 0.439692 1.784733\n",
"17 0.752529 0.0 0.420920 0.800005 5.000115\n",
"18 0.448100 0.0 2.359522 0.714566 3.503436\n",
"19 0.738845 0.0 1.873614 0.796613 4.916733\n",
"20 -1.378189 0.0 -2.404268 0.130805 1.150490\n",
"21 0.367733 0.0 -1.362551 0.688677 3.212097\n",
"22 -0.677443 0.0 1.005151 0.306809 1.442605\n",
"23 -1.074204 0.0 -0.822769 0.193741 1.240297\n",
"24 -0.728784 0.0 0.335534 0.290261 1.408969\n",
"25 -0.174916 0.0 -0.816614 0.489638 1.959395\n",
"26 0.131783 0.0 -0.348114 0.606042 2.538339\n",
"27 0.407820 0.0 0.111378 0.701752 3.352911\n",
"28 -1.384935 0.0 -3.563577 0.129629 1.148935\n",
"29 -2.530571 0.0 -2.978433 0.024894 1.025530\n",
".. ... ... ... ... ...\n",
"970 -0.955608 0.0 -3.682694 0.223861 1.288429\n",
"971 -2.717983 0.0 -4.138503 0.018772 1.019131\n",
"972 -0.107787 0.0 0.199005 0.515468 2.063847\n",
"973 -1.650197 0.0 -1.115839 0.090084 1.099003\n",
"974 -1.648183 0.0 -1.432377 0.090339 1.099310\n",
"975 0.288879 0.0 2.644426 0.662073 2.959223\n",
"976 -0.657748 0.0 0.878296 0.313295 1.456230\n",
"977 -0.186507 0.0 0.739407 0.485180 1.942428\n",
"978 -0.088590 0.0 -0.597798 0.522845 2.095755\n",
"979 -1.200004 0.0 0.342928 0.165267 1.197989\n",
"980 0.520743 0.0 -0.433905 0.736818 3.799653\n",
"981 0.536033 0.0 0.475667 0.741357 3.866334\n",
"982 -0.031386 0.0 0.975553 0.544757 2.196629\n",
"983 0.700180 0.0 0.390546 0.786798 4.690394\n",
"984 0.353783 0.0 0.587299 0.684054 3.165097\n",
"985 0.592212 0.0 0.788882 0.757592 4.125268\n",
"986 0.317355 0.0 -1.466783 0.671811 3.047021\n",
"987 0.281453 0.0 1.043265 0.659511 2.936951\n",
"988 -1.740564 0.0 -2.890607 0.079313 1.086145\n",
"989 -0.751195 0.0 -0.083296 0.283205 1.395100\n",
"990 -1.057706 0.0 -0.308057 0.197740 1.246478\n",
"991 -0.553931 0.0 -1.044103 0.348660 1.535296\n",
"992 0.192048 0.0 -1.286848 0.627961 2.687891\n",
"993 -1.118731 0.0 -1.095691 0.183258 1.224377\n",
"994 -0.917039 0.0 -0.857244 0.234346 1.306074\n",
"995 -2.030707 0.0 -2.866590 0.052233 1.055112\n",
"996 0.181139 0.0 0.942937 0.624029 2.659781\n",
"997 0.125104 0.0 -0.041996 0.603584 2.522603\n",
"998 -0.720169 0.0 -0.553785 0.293001 1.414429\n",
"999 0.081376 0.0 0.596130 0.587370 2.423478\n",
"\n",
"[1000 rows x 5 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf_0"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X0</th>\n",
" <th>v</th>\n",
" <th>y</th>\n",
" <th>propensity_score</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1.951443</td>\n",
" <td>1.0</td>\n",
" <td>1.311154</td>\n",
" <td>0.058615</td>\n",
" <td>17.060529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.036153</td>\n",
" <td>1.0</td>\n",
" <td>4.948191</td>\n",
" <td>0.542936</td>\n",
" <td>1.841836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-2.093958</td>\n",
" <td>1.0</td>\n",
" <td>1.872111</td>\n",
" <td>0.047618</td>\n",
" <td>21.000604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1.618492</td>\n",
" <td>1.0</td>\n",
" <td>3.598020</td>\n",
" <td>0.094166</td>\n",
" <td>10.619537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.950544</td>\n",
" <td>1.0</td>\n",
" <td>3.323188</td>\n",
" <td>0.058691</td>\n",
" <td>17.038313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1.203633</td>\n",
" <td>1.0</td>\n",
" <td>7.341404</td>\n",
" <td>0.889022</td>\n",
" <td>1.124832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-2.212717</td>\n",
" <td>1.0</td>\n",
" <td>2.434425</td>\n",
" <td>0.039979</td>\n",
" <td>25.012842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.483879</td>\n",
" <td>1.0</td>\n",
" <td>5.664137</td>\n",
" <td>0.373533</td>\n",
" <td>2.677142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1.248253</td>\n",
" <td>1.0</td>\n",
" <td>6.994602</td>\n",
" <td>0.895620</td>\n",
" <td>1.116545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-0.003459</td>\n",
" <td>1.0</td>\n",
" <td>5.208806</td>\n",
" <td>0.555397</td>\n",
" <td>1.800514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-0.483879</td>\n",
" <td>1.0</td>\n",
" <td>5.664137</td>\n",
" <td>0.373533</td>\n",
" <td>2.677142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>-1.405906</td>\n",
" <td>1.0</td>\n",
" <td>1.364267</td>\n",
" <td>0.126030</td>\n",
" <td>7.934625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.462737</td>\n",
" <td>1.0</td>\n",
" <td>4.975272</td>\n",
" <td>0.719140</td>\n",
" <td>1.390551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>-1.462970</td>\n",
" <td>1.0</td>\n",
" <td>2.789579</td>\n",
" <td>0.116667</td>\n",
" <td>8.571375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.258047</td>\n",
" <td>1.0</td>\n",
" <td>6.714741</td>\n",
" <td>0.651374</td>\n",
" <td>1.535217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>-1.607051</td>\n",
" <td>1.0</td>\n",
" <td>2.544114</td>\n",
" <td>0.095679</td>\n",
" <td>10.451596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>-1.462970</td>\n",
" <td>1.0</td>\n",
" <td>2.789579</td>\n",
" <td>0.116667</td>\n",
" <td>8.571375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>-2.093958</td>\n",
" <td>1.0</td>\n",
" <td>1.872111</td>\n",
" <td>0.047618</td>\n",
" <td>21.000604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>-1.535155</td>\n",
" <td>1.0</td>\n",
" <td>2.576505</td>\n",
" <td>0.105694</td>\n",
" <td>9.461285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-0.618529</td>\n",
" <td>1.0</td>\n",
" <td>3.450191</td>\n",
" <td>0.326429</td>\n",
" <td>3.063455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>-1.950544</td>\n",
" <td>1.0</td>\n",
" <td>3.323188</td>\n",
" <td>0.058691</td>\n",
" <td>17.038313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-0.057918</td>\n",
" <td>1.0</td>\n",
" <td>3.290478</td>\n",
" <td>0.534610</td>\n",
" <td>1.870522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-0.806616</td>\n",
" <td>1.0</td>\n",
" <td>4.136377</td>\n",
" <td>0.266210</td>\n",
" <td>3.756429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.984550</td>\n",
" <td>1.0</td>\n",
" <td>4.536581</td>\n",
" <td>0.851133</td>\n",
" <td>1.174905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>-0.276314</td>\n",
" <td>1.0</td>\n",
" <td>4.694700</td>\n",
" <td>0.450772</td>\n",
" <td>2.218415</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.304571</td>\n",
" <td>1.0</td>\n",
" <td>5.037979</td>\n",
" <td>0.667457</td>\n",
" <td>1.498224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-0.410024</td>\n",
" <td>1.0</td>\n",
" <td>5.215263</td>\n",
" <td>0.400497</td>\n",
" <td>2.496895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>-1.245270</td>\n",
" <td>1.0</td>\n",
" <td>3.452331</td>\n",
" <td>0.155877</td>\n",
" <td>6.415317</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>-1.208599</td>\n",
" <td>1.0</td>\n",
" <td>3.558146</td>\n",
" <td>0.163450</td>\n",
" <td>6.118068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-0.057918</td>\n",
" <td>1.0</td>\n",
" <td>3.290478</td>\n",
" <td>0.534610</td>\n",
" <td>1.870522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>970</th>\n",
" <td>-0.994023</td>\n",
" <td>1.0</td>\n",
" <td>2.741115</td>\n",
" <td>0.213754</td>\n",
" <td>4.678274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>971</th>\n",
" <td>-1.404664</td>\n",
" <td>1.0</td>\n",
" <td>3.818183</td>\n",
" <td>0.126241</td>\n",
" <td>7.921370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>972</th>\n",
" <td>-0.888410</td>\n",
" <td>1.0</td>\n",
" <td>4.120169</td>\n",
" <td>0.242347</td>\n",
" <td>4.126316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>973</th>\n",
" <td>-2.212717</td>\n",
" <td>1.0</td>\n",
" <td>2.434425</td>\n",
" <td>0.039979</td>\n",
" <td>25.012842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>974</th>\n",
" <td>-1.036326</td>\n",
" <td>1.0</td>\n",
" <td>3.084976</td>\n",
" <td>0.203013</td>\n",
" <td>4.925794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>975</th>\n",
" <td>0.294169</td>\n",
" <td>1.0</td>\n",
" <td>5.803374</td>\n",
" <td>0.663893</td>\n",
" <td>1.506267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>976</th>\n",
" <td>0.044636</td>\n",
" <td>1.0</td>\n",
" <td>4.985137</td>\n",
" <td>0.573597</td>\n",
" <td>1.743384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>977</th>\n",
" <td>-1.404664</td>\n",
" <td>1.0</td>\n",
" <td>3.818183</td>\n",
" <td>0.126241</td>\n",
" <td>7.921370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>978</th>\n",
" <td>0.178626</td>\n",
" <td>1.0</td>\n",
" <td>4.351284</td>\n",
" <td>0.623121</td>\n",
" <td>1.604825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>979</th>\n",
" <td>-1.832697</td>\n",
" <td>1.0</td>\n",
" <td>3.132228</td>\n",
" <td>0.069554</td>\n",
" <td>14.377294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>980</th>\n",
" <td>0.365069</td>\n",
" <td>1.0</td>\n",
" <td>2.910219</td>\n",
" <td>0.687797</td>\n",
" <td>1.453917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>981</th>\n",
" <td>-0.548951</td>\n",
" <td>1.0</td>\n",
" <td>5.208767</td>\n",
" <td>0.350403</td>\n",
" <td>2.853857</td>\n",
" </tr>\n",
" <tr>\n",
" <th>982</th>\n",
" <td>0.341563</td>\n",
" <td>1.0</td>\n",
" <td>5.859356</td>\n",
" <td>0.679974</td>\n",
" <td>1.470644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>983</th>\n",
" <td>-1.459629</td>\n",
" <td>1.0</td>\n",
" <td>2.510587</td>\n",
" <td>0.117198</td>\n",
" <td>8.532536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>984</th>\n",
" <td>-0.747720</td>\n",
" <td>1.0</td>\n",
" <td>5.979018</td>\n",
" <td>0.284293</td>\n",
" <td>3.517502</td>\n",
" </tr>\n",
" <tr>\n",
" <th>985</th>\n",
" <td>0.417509</td>\n",
" <td>1.0</td>\n",
" <td>6.463046</td>\n",
" <td>0.704864</td>\n",
" <td>1.418713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>986</th>\n",
" <td>-0.877568</td>\n",
" <td>1.0</td>\n",
" <td>4.498496</td>\n",
" <td>0.245425</td>\n",
" <td>4.074567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>987</th>\n",
" <td>0.879451</td>\n",
" <td>1.0</td>\n",
" <td>5.161535</td>\n",
" <td>0.829448</td>\n",
" <td>1.205622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>988</th>\n",
" <td>0.861876</td>\n",
" <td>1.0</td>\n",
" <td>6.641470</td>\n",
" <td>0.825586</td>\n",
" <td>1.211261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>989</th>\n",
" <td>0.149097</td>\n",
" <td>1.0</td>\n",
" <td>5.979875</td>\n",
" <td>0.612387</td>\n",
" <td>1.632955</td>\n",
" </tr>\n",
" <tr>\n",
" <th>990</th>\n",
" <td>-0.918103</td>\n",
" <td>1.0</td>\n",
" <td>3.682658</td>\n",
" <td>0.234053</td>\n",
" <td>4.272541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>991</th>\n",
" <td>-0.272513</td>\n",
" <td>1.0</td>\n",
" <td>5.578401</td>\n",
" <td>0.452221</td>\n",
" <td>2.211306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>992</th>\n",
" <td>-1.832697</td>\n",
" <td>1.0</td>\n",
" <td>3.132228</td>\n",
" <td>0.069554</td>\n",
" <td>14.377294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>993</th>\n",
" <td>-1.462970</td>\n",
" <td>1.0</td>\n",
" <td>2.789579</td>\n",
" <td>0.116667</td>\n",
" <td>8.571375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>994</th>\n",
" <td>0.294169</td>\n",
" <td>1.0</td>\n",
" <td>5.803374</td>\n",
" <td>0.663893</td>\n",
" <td>1.506267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>0.496898</td>\n",
" <td>1.0</td>\n",
" <td>6.138053</td>\n",
" <td>0.729638</td>\n",
" <td>1.370542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>-0.490023</td>\n",
" <td>1.0</td>\n",
" <td>2.314310</td>\n",
" <td>0.371322</td>\n",
" <td>2.693081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>0.159245</td>\n",
" <td>1.0</td>\n",
" <td>4.808394</td>\n",
" <td>0.616089</td>\n",
" <td>1.623142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>0.417509</td>\n",
" <td>1.0</td>\n",
" <td>6.463046</td>\n",
" <td>0.704864</td>\n",
" <td>1.418713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>-1.005815</td>\n",
" <td>1.0</td>\n",
" <td>2.865650</td>\n",
" <td>0.210719</td>\n",
" <td>4.745661</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" X0 v y propensity_score weight\n",
"0 -1.951443 1.0 1.311154 0.058615 17.060529\n",
"1 -0.036153 1.0 4.948191 0.542936 1.841836\n",
"2 -2.093958 1.0 1.872111 0.047618 21.000604\n",
"3 -1.618492 1.0 3.598020 0.094166 10.619537\n",
"4 -1.950544 1.0 3.323188 0.058691 17.038313\n",
"5 1.203633 1.0 7.341404 0.889022 1.124832\n",
"6 -2.212717 1.0 2.434425 0.039979 25.012842\n",
"7 -0.483879 1.0 5.664137 0.373533 2.677142\n",
"8 1.248253 1.0 6.994602 0.895620 1.116545\n",
"9 -0.003459 1.0 5.208806 0.555397 1.800514\n",
"10 -0.483879 1.0 5.664137 0.373533 2.677142\n",
"11 -1.405906 1.0 1.364267 0.126030 7.934625\n",
"12 0.462737 1.0 4.975272 0.719140 1.390551\n",
"13 -1.462970 1.0 2.789579 0.116667 8.571375\n",
"14 0.258047 1.0 6.714741 0.651374 1.535217\n",
"15 -1.607051 1.0 2.544114 0.095679 10.451596\n",
"16 -1.462970 1.0 2.789579 0.116667 8.571375\n",
"17 -2.093958 1.0 1.872111 0.047618 21.000604\n",
"18 -1.535155 1.0 2.576505 0.105694 9.461285\n",
"19 -0.618529 1.0 3.450191 0.326429 3.063455\n",
"20 -1.950544 1.0 3.323188 0.058691 17.038313\n",
"21 -0.057918 1.0 3.290478 0.534610 1.870522\n",
"22 -0.806616 1.0 4.136377 0.266210 3.756429\n",
"23 0.984550 1.0 4.536581 0.851133 1.174905\n",
"24 -0.276314 1.0 4.694700 0.450772 2.218415\n",
"25 0.304571 1.0 5.037979 0.667457 1.498224\n",
"26 -0.410024 1.0 5.215263 0.400497 2.496895\n",
"27 -1.245270 1.0 3.452331 0.155877 6.415317\n",
"28 -1.208599 1.0 3.558146 0.163450 6.118068\n",
"29 -0.057918 1.0 3.290478 0.534610 1.870522\n",
".. ... ... ... ... ...\n",
"970 -0.994023 1.0 2.741115 0.213754 4.678274\n",
"971 -1.404664 1.0 3.818183 0.126241 7.921370\n",
"972 -0.888410 1.0 4.120169 0.242347 4.126316\n",
"973 -2.212717 1.0 2.434425 0.039979 25.012842\n",
"974 -1.036326 1.0 3.084976 0.203013 4.925794\n",
"975 0.294169 1.0 5.803374 0.663893 1.506267\n",
"976 0.044636 1.0 4.985137 0.573597 1.743384\n",
"977 -1.404664 1.0 3.818183 0.126241 7.921370\n",
"978 0.178626 1.0 4.351284 0.623121 1.604825\n",
"979 -1.832697 1.0 3.132228 0.069554 14.377294\n",
"980 0.365069 1.0 2.910219 0.687797 1.453917\n",
"981 -0.548951 1.0 5.208767 0.350403 2.853857\n",
"982 0.341563 1.0 5.859356 0.679974 1.470644\n",
"983 -1.459629 1.0 2.510587 0.117198 8.532536\n",
"984 -0.747720 1.0 5.979018 0.284293 3.517502\n",
"985 0.417509 1.0 6.463046 0.704864 1.418713\n",
"986 -0.877568 1.0 4.498496 0.245425 4.074567\n",
"987 0.879451 1.0 5.161535 0.829448 1.205622\n",
"988 0.861876 1.0 6.641470 0.825586 1.211261\n",
"989 0.149097 1.0 5.979875 0.612387 1.632955\n",
"990 -0.918103 1.0 3.682658 0.234053 4.272541\n",
"991 -0.272513 1.0 5.578401 0.452221 2.211306\n",
"992 -1.832697 1.0 3.132228 0.069554 14.377294\n",
"993 -1.462970 1.0 2.789579 0.116667 8.571375\n",
"994 0.294169 1.0 5.803374 0.663893 1.506267\n",
"995 0.496898 1.0 6.138053 0.729638 1.370542\n",
"996 -0.490023 1.0 2.314310 0.371322 2.693081\n",
"997 0.159245 1.0 4.808394 0.616089 1.623142\n",
"998 0.417509 1.0 6.463046 0.704864 1.418713\n",
"999 -1.005815 1.0 2.865650 0.210719 4.745661\n",
"\n",
"[1000 rows x 5 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf_1"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:dowhy.do_why:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n",
"INFO:dowhy.causal_identifier:Common causes of treatment and outcome:{'X0', 'U'}\n",
"INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
"INFO:dowhy.do_sampler:Using WeightingSampler for do sampling.\n",
"INFO:dowhy.do_sampler:Caution: do samplers assume iid data.\n",
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n",
"WARNING:dowhy.do_why:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n",
"INFO:dowhy.causal_identifier:Common causes of treatment and outcome:{'X0', 'U'}\n",
"INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
"INFO:dowhy.do_sampler:Using WeightingSampler for do sampling.\n",
"INFO:dowhy.do_sampler:Caution: do samplers assume iid data.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['X0']\n",
"yes\n",
"{'observed': 'yes'}\n",
"Model to find the causal effect of treatment v on outcome y\n",
"{'observed': 'yes'}\n",
"{'label': 'Unobserved Confounders', 'observed': 'no'}\n",
"All common causes are observed. Causal effect can be identified.\n",
"WeightingSampler\n",
"treatments ['v']\n",
"backdoor ['X0']\n",
"['X0']\n",
"yes\n",
"{'observed': 'yes'}\n",
"Model to find the causal effect of treatment v on outcome y\n",
"{'observed': 'yes'}\n",
"{'label': 'Unobserved Confounders', 'observed': 'no'}\n",
"All common causes are observed. Causal effect can be identified.\n",
"WeightingSampler\n",
"treatments ['v']\n",
"backdoor ['X0']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
}
],
"source": [
"cdf_1 = cdf.causal.do(x={'v': 1}, \n",
" variable_types={'v': 'b', 'y': 'c', 'X0': 'c'}, \n",
" outcome='y',\n",
" method='weighting', \n",
" common_causes=['X0'],\n",
" proceed_when_unidentifiable=True,\n",
" use_previous_sampler=False)\n",
"cdf_0 = cdf.causal.do(x={'v': 0}, \n",
" variable_types={'v': 'b', 'y': 'c', 'X0': 'c'}, \n",
" outcome='y',\n",
" method='weighting', \n",
" common_causes=['X0'],\n",
" proceed_when_unidentifiable=True,\n",
" use_previous_sampler=False)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"X0 0.122923\n",
"v 1.000000\n",
"y 5.207919\n",
"propensity_score 0.018638\n",
"weight 3.347780\n",
"dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(cdf_1 - cdf_0).mean()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"X0 0.122923\n",
"v 1.000000\n",
"y 5.207919\n",
"propensity_score 0.018638\n",
"weight 3.347780\n",
"dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf_1.mean() - cdf_0.mean()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/latex": [
"$$\\left ( 1000, \\quad 1000\\right )$$"
],
"text/plain": [
"(1000, 1000)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(cdf_1), len(cdf_0)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/latex": [
"$$0.1280663414610198$$"
],
"text/plain": [
"0.1280663414610198"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1.96*(cdf_1['y'] - cdf_0['y']).std() / np.sqrt(len(cdf))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> R-squared: </th> <td> 0.927</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.926</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 6297.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 16 Feb 2019</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>23:20:22</td> <th> Log-Likelihood: </th> <td> -1391.4</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 1000</td> <th> AIC: </th> <td> 2787.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 998</td> <th> BIC: </th> <td> 2797.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 2</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<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>X0</th> <td> 1.2060</td> <td> 0.026</td> <td> 46.320</td> <td> 0.000</td> <td> 1.155</td> <td> 1.257</td>\n",
"</tr>\n",
"<tr>\n",
" <th>v</th> <td> 4.9865</td> <td> 0.050</td> <td> 100.115</td> <td> 0.000</td> <td> 4.889</td> <td> 5.084</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 4.266</td> <th> Durbin-Watson: </th> <td> 1.928</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.119</td> <th> Jarque-Bera (JB): </th> <td> 4.341</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td>-0.155</td> <th> Prob(JB): </th> <td> 0.114</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 2.909</td> <th> Cond. No. </th> <td> 1.92</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.927\n",
"Model: OLS Adj. R-squared: 0.926\n",
"Method: Least Squares F-statistic: 6297.\n",
"Date: Sat, 16 Feb 2019 Prob (F-statistic): 0.00\n",
"Time: 23:20:22 Log-Likelihood: -1391.4\n",
"No. Observations: 1000 AIC: 2787.\n",
"Df Residuals: 998 BIC: 2797.\n",
"Df Model: 2 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"X0 1.2060 0.026 46.320 0.000 1.155 1.257\n",
"v 4.9865 0.050 100.115 0.000 4.889 5.084\n",
"==============================================================================\n",
"Omnibus: 4.266 Durbin-Watson: 1.928\n",
"Prob(Omnibus): 0.119 Jarque-Bera (JB): 4.341\n",
"Skew: -0.155 Prob(JB): 0.114\n",
"Kurtosis: 2.909 Cond. No. 1.92\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = OLS(df['y'], df[['X0', 'v']])\n",
"result = model.fit()\n",
"result.summary()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"X0 -0.556400\n",
"v 1.000000\n",
"y 4.341221\n",
"propensity_score 0.385928\n",
"weight 5.360308\n",
"dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf_1.mean()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"X0 -0.679323\n",
"v 0.000000\n",
"y -0.866698\n",
"propensity_score 0.367290\n",
"weight 2.012528\n",
"dtype: float64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf_0.mean()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:dowhy.do_why:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n",
"INFO:dowhy.causal_identifier:Common causes of treatment and outcome:{'X0', 'U'}\n",
"INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n",
"INFO:dowhy.do_sampler:Using WeightingSampler for do sampling.\n",
"INFO:dowhy.do_sampler:Caution: do samplers assume iid data.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['X0']\n",
"yes\n",
"{'observed': 'yes'}\n",
"Model to find the causal effect of treatment v on outcome y\n",
"{'observed': 'yes'}\n",
"{'label': 'Unobserved Confounders', 'observed': 'no'}\n",
"All common causes are observed. Causal effect can be identified.\n",
"WeightingSampler\n",
"treatments ['v']\n",
"backdoor ['X0']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/home/akelleh/.local/lib/python3.6/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
}
],
"source": [
"cdf_do = cdf.causal.do(x={'v': 0}, \n",
" variable_types={'v': 'b', 'y': 'c', 'X0': 'c'}, \n",
" outcome='y',\n",
" method='weighting', \n",
" common_causes=['X0'],\n",
" proceed_when_unidentifiable=True,\n",
" keep_original_treatment=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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" <td>0.069554</td>\n",
" <td>14.377294</td>\n",
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" <td>2.544114</td>\n",
" <td>0.095679</td>\n",
" <td>10.451596</td>\n",
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" <th>12</th>\n",
" <td>-1.950544</td>\n",
" <td>1.0</td>\n",
" <td>3.323188</td>\n",
" <td>0.058691</td>\n",
" <td>17.038313</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>0.149788</td>\n",
" <td>1.0</td>\n",
" <td>3.684576</td>\n",
" <td>0.612639</td>\n",
" <td>1.632281</td>\n",
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" <td>0.0</td>\n",
" <td>-0.994245</td>\n",
" <td>0.038830</td>\n",
" <td>1.040399</td>\n",
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" <td>0.0</td>\n",
" <td>-1.252407</td>\n",
" <td>0.282998</td>\n",
" <td>1.394695</td>\n",
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" <th>16</th>\n",
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" <td>1.0</td>\n",
" <td>2.741115</td>\n",
" <td>0.213754</td>\n",
" <td>4.678274</td>\n",
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" <td>0.0</td>\n",
" <td>-2.461482</td>\n",
" <td>0.076443</td>\n",
" <td>1.082770</td>\n",
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" <td>-2.212717</td>\n",
" <td>1.0</td>\n",
" <td>2.434425</td>\n",
" <td>0.039979</td>\n",
" <td>25.012842</td>\n",
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" <td>-0.918103</td>\n",
" <td>1.0</td>\n",
" <td>3.682658</td>\n",
" <td>0.234053</td>\n",
" <td>4.272541</td>\n",
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" <td>0.0</td>\n",
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" <td>0.743694</td>\n",
" <td>3.901583</td>\n",
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" <td>1.0</td>\n",
" <td>3.504141</td>\n",
" <td>0.181674</td>\n",
" <td>5.504358</td>\n",
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" <td>0.0</td>\n",
" <td>1.996327</td>\n",
" <td>0.743694</td>\n",
" <td>3.901583</td>\n",
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" <tr>\n",
" <th>23</th>\n",
" <td>0.977635</td>\n",
" <td>0.0</td>\n",
" <td>2.118892</td>\n",
" <td>0.849779</td>\n",
" <td>6.656852</td>\n",
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" <tr>\n",
" <th>24</th>\n",
" <td>-1.581382</td>\n",
" <td>1.0</td>\n",
" <td>2.957852</td>\n",
" <td>0.099153</td>\n",
" <td>10.085387</td>\n",
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" <td>0.131783</td>\n",
" <td>0.0</td>\n",
" <td>-0.348114</td>\n",
" <td>0.606042</td>\n",
" <td>2.538339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-0.311453</td>\n",
" <td>1.0</td>\n",
" <td>5.269244</td>\n",
" <td>0.437418</td>\n",
" <td>2.286141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.273382</td>\n",
" <td>0.0</td>\n",
" <td>-0.149007</td>\n",
" <td>0.656715</td>\n",
" <td>2.913034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.050845</td>\n",
" <td>1.0</td>\n",
" <td>5.003995</td>\n",
" <td>0.575933</td>\n",
" <td>1.736313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-1.404664</td>\n",
" <td>1.0</td>\n",
" <td>3.818183</td>\n",
" <td>0.126241</td>\n",
" <td>7.921370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>970</th>\n",
" <td>-0.246277</td>\n",
" <td>0.0</td>\n",
" <td>-1.322790</td>\n",
" <td>0.462245</td>\n",
" <td>1.859581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>971</th>\n",
" <td>-0.235342</td>\n",
" <td>1.0</td>\n",
" <td>3.306528</td>\n",
" <td>0.466432</td>\n",
" <td>2.143936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>972</th>\n",
" <td>-1.714516</td>\n",
" <td>0.0</td>\n",
" <td>-2.556439</td>\n",
" <td>0.082291</td>\n",
" <td>1.089670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>973</th>\n",
" <td>0.288059</td>\n",
" <td>1.0</td>\n",
" <td>5.972127</td>\n",
" <td>0.661791</td>\n",
" <td>1.511051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>974</th>\n",
" <td>-0.331371</td>\n",
" <td>0.0</td>\n",
" <td>1.835507</td>\n",
" <td>0.429887</td>\n",
" <td>1.754040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>975</th>\n",
" <td>1.660624</td>\n",
" <td>1.0</td>\n",
" <td>7.130846</td>\n",
" <td>0.941822</td>\n",
" <td>1.061772</td>\n",
" </tr>\n",
" <tr>\n",
" <th>976</th>\n",
" <td>0.040006</td>\n",
" <td>0.0</td>\n",
" <td>-1.684959</td>\n",
" <td>0.571853</td>\n",
" <td>2.335644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>977</th>\n",
" <td>-0.766631</td>\n",
" <td>0.0</td>\n",
" <td>-1.258390</td>\n",
" <td>0.278406</td>\n",
" <td>1.385822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>978</th>\n",
" <td>-1.567630</td>\n",
" <td>0.0</td>\n",
" <td>-1.844169</td>\n",
" <td>0.101060</td>\n",
" <td>1.112422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>979</th>\n",
" <td>0.543972</td>\n",
" <td>0.0</td>\n",
" <td>1.996327</td>\n",
" <td>0.743694</td>\n",
" <td>3.901583</td>\n",
" </tr>\n",
" <tr>\n",
" <th>980</th>\n",
" <td>0.472116</td>\n",
" <td>0.0</td>\n",
" <td>1.263226</td>\n",
" <td>0.722047</td>\n",
" <td>3.597724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>981</th>\n",
" <td>-0.014672</td>\n",
" <td>0.0</td>\n",
" <td>-1.239185</td>\n",
" <td>0.551130</td>\n",
" <td>2.227819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>982</th>\n",
" <td>-2.175988</td>\n",
" <td>0.0</td>\n",
" <td>-3.126303</td>\n",
" <td>0.042207</td>\n",
" <td>1.044067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>983</th>\n",
" <td>-0.799477</td>\n",
" <td>0.0</td>\n",
" <td>-1.530741</td>\n",
" <td>0.268363</td>\n",
" <td>1.366797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>984</th>\n",
" <td>-1.190321</td>\n",
" <td>0.0</td>\n",
" <td>-2.409672</td>\n",
" <td>0.167334</td>\n",
" <td>1.200962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>985</th>\n",
" <td>-0.161634</td>\n",
" <td>1.0</td>\n",
" <td>4.715751</td>\n",
" <td>0.494749</td>\n",
" <td>2.021227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>986</th>\n",
" <td>-0.152092</td>\n",
" <td>0.0</td>\n",
" <td>0.443881</td>\n",
" <td>0.498421</td>\n",
" <td>1.993705</td>\n",
" </tr>\n",
" <tr>\n",
" <th>987</th>\n",
" <td>0.536033</td>\n",
" <td>0.0</td>\n",
" <td>0.475667</td>\n",
" <td>0.741357</td>\n",
" <td>3.866334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>988</th>\n",
" <td>-1.950544</td>\n",
" <td>1.0</td>\n",
" <td>3.323188</td>\n",
" <td>0.058691</td>\n",
" <td>17.038313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>989</th>\n",
" <td>-0.440491</td>\n",
" <td>1.0</td>\n",
" <td>3.875684</td>\n",
" <td>0.389290</td>\n",
" <td>2.568778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>990</th>\n",
" <td>-1.217770</td>\n",
" <td>0.0</td>\n",
" <td>-0.813250</td>\n",
" <td>0.161529</td>\n",
" <td>1.192647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>991</th>\n",
" <td>-0.056032</td>\n",
" <td>0.0</td>\n",
" <td>-0.528141</td>\n",
" <td>0.535333</td>\n",
" <td>2.152078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>992</th>\n",
" <td>0.524571</td>\n",
" <td>0.0</td>\n",
" <td>-0.074485</td>\n",
" <td>0.737959</td>\n",
" <td>3.816200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>993</th>\n",
" <td>0.612730</td>\n",
" <td>0.0</td>\n",
" <td>0.101522</td>\n",
" <td>0.763345</td>\n",
" <td>4.225564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>994</th>\n",
" <td>0.752529</td>\n",
" <td>0.0</td>\n",
" <td>0.420920</td>\n",
" <td>0.800005</td>\n",
" <td>5.000115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>0.218947</td>\n",
" <td>1.0</td>\n",
" <td>6.080291</td>\n",
" <td>0.637583</td>\n",
" <td>1.568423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>-0.930352</td>\n",
" <td>0.0</td>\n",
" <td>-0.293165</td>\n",
" <td>0.230689</td>\n",
" <td>1.299865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>0.397293</td>\n",
" <td>0.0</td>\n",
" <td>-0.175455</td>\n",
" <td>0.698349</td>\n",
" <td>3.315089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>-1.283557</td>\n",
" <td>0.0</td>\n",
" <td>-2.046988</td>\n",
" <td>0.148278</td>\n",
" <td>1.174092</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>0.288879</td>\n",
" <td>0.0</td>\n",
" <td>2.644426</td>\n",
" <td>0.662073</td>\n",
" <td>2.959223</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" X0 v y propensity_score weight\n",
"0 -0.779668 0.0 -2.664266 0.274392 1.378155\n",
"1 -0.331371 0.0 1.835507 0.429887 1.754040\n",
"2 -0.697411 0.0 -0.658924 0.300311 1.429206\n",
"3 -1.618492 1.0 3.598020 0.094166 10.619537\n",
"4 0.527050 0.0 1.776464 0.738697 3.826970\n",
"5 0.088109 1.0 3.036698 0.589879 1.695262\n",
"6 -0.548951 1.0 5.208767 0.350403 2.853857\n",
"7 0.208574 0.0 0.765640 0.633885 2.731382\n",
"8 -1.991554 0.0 -2.424806 0.055299 1.058536\n",
"9 1.135281 0.0 -0.276102 0.878207 8.210658\n",
"10 -1.832697 1.0 3.132228 0.069554 14.377294\n",
"11 -1.607051 1.0 2.544114 0.095679 10.451596\n",
"12 -1.950544 1.0 3.323188 0.058691 17.038313\n",
"13 0.149788 1.0 3.684576 0.612639 1.632281\n",
"14 -2.232443 0.0 -0.994245 0.038830 1.040399\n",
"15 -0.751860 0.0 -1.252407 0.282998 1.394695\n",
"16 -0.994023 1.0 2.741115 0.213754 4.678274\n",
"17 -1.766522 0.0 -2.461482 0.076443 1.082770\n",
"18 -2.212717 1.0 2.434425 0.039979 25.012842\n",
"19 -0.918103 1.0 3.682658 0.234053 4.272541\n",
"20 0.543972 0.0 1.996327 0.743694 3.901583\n",
"21 -1.125627 1.0 3.504141 0.181674 5.504358\n",
"22 0.543972 0.0 1.996327 0.743694 3.901583\n",
"23 0.977635 0.0 2.118892 0.849779 6.656852\n",
"24 -1.581382 1.0 2.957852 0.099153 10.085387\n",
"25 0.131783 0.0 -0.348114 0.606042 2.538339\n",
"26 -0.311453 1.0 5.269244 0.437418 2.286141\n",
"27 0.273382 0.0 -0.149007 0.656715 2.913034\n",
"28 0.050845 1.0 5.003995 0.575933 1.736313\n",
"29 -1.404664 1.0 3.818183 0.126241 7.921370\n",
".. ... ... ... ... ...\n",
"970 -0.246277 0.0 -1.322790 0.462245 1.859581\n",
"971 -0.235342 1.0 3.306528 0.466432 2.143936\n",
"972 -1.714516 0.0 -2.556439 0.082291 1.089670\n",
"973 0.288059 1.0 5.972127 0.661791 1.511051\n",
"974 -0.331371 0.0 1.835507 0.429887 1.754040\n",
"975 1.660624 1.0 7.130846 0.941822 1.061772\n",
"976 0.040006 0.0 -1.684959 0.571853 2.335644\n",
"977 -0.766631 0.0 -1.258390 0.278406 1.385822\n",
"978 -1.567630 0.0 -1.844169 0.101060 1.112422\n",
"979 0.543972 0.0 1.996327 0.743694 3.901583\n",
"980 0.472116 0.0 1.263226 0.722047 3.597724\n",
"981 -0.014672 0.0 -1.239185 0.551130 2.227819\n",
"982 -2.175988 0.0 -3.126303 0.042207 1.044067\n",
"983 -0.799477 0.0 -1.530741 0.268363 1.366797\n",
"984 -1.190321 0.0 -2.409672 0.167334 1.200962\n",
"985 -0.161634 1.0 4.715751 0.494749 2.021227\n",
"986 -0.152092 0.0 0.443881 0.498421 1.993705\n",
"987 0.536033 0.0 0.475667 0.741357 3.866334\n",
"988 -1.950544 1.0 3.323188 0.058691 17.038313\n",
"989 -0.440491 1.0 3.875684 0.389290 2.568778\n",
"990 -1.217770 0.0 -0.813250 0.161529 1.192647\n",
"991 -0.056032 0.0 -0.528141 0.535333 2.152078\n",
"992 0.524571 0.0 -0.074485 0.737959 3.816200\n",
"993 0.612730 0.0 0.101522 0.763345 4.225564\n",
"994 0.752529 0.0 0.420920 0.800005 5.000115\n",
"995 0.218947 1.0 6.080291 0.637583 1.568423\n",
"996 -0.930352 0.0 -0.293165 0.230689 1.299865\n",
"997 0.397293 0.0 -0.175455 0.698349 3.315089\n",
"998 -1.283557 0.0 -2.046988 0.148278 1.174092\n",
"999 0.288879 0.0 2.644426 0.662073 2.959223\n",
"\n",
"[1000 rows x 5 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf_do"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>X0</th>\n",
" <th>v</th>\n",
" <th>y</th>\n",
" <th>propensity_score</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.059672</td>\n",
" <td>1.0</td>\n",
" <td>5.719181</td>\n",
" <td>0.865199</td>\n",
" <td>1.155803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.161911</td>\n",
" <td>1.0</td>\n",
" <td>3.738860</td>\n",
" <td>0.173517</td>\n",
" <td>5.763117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.720072</td>\n",
" <td>1.0</td>\n",
" <td>4.821778</td>\n",
" <td>0.293032</td>\n",
" <td>3.412596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1.369444</td>\n",
" <td>1.0</td>\n",
" <td>3.066326</td>\n",
" <td>0.132344</td>\n",
" <td>7.556092</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.372525</td>\n",
" <td>1.0</td>\n",
" <td>5.355411</td>\n",
" <td>0.414434</td>\n",
" <td>2.412929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-1.405906</td>\n",
" <td>1.0</td>\n",
" <td>1.364267</td>\n",
" <td>0.126030</td>\n",
" <td>7.934625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.133946</td>\n",
" <td>1.0</td>\n",
" <td>5.715764</td>\n",
" <td>0.505405</td>\n",
" <td>1.978611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.924519</td>\n",
" <td>1.0</td>\n",
" <td>3.134797</td>\n",
" <td>0.232287</td>\n",
" <td>4.305027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-1.950544</td>\n",
" <td>1.0</td>\n",
" <td>3.323188</td>\n",
" <td>0.058691</td>\n",
" <td>17.038313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-1.404664</td>\n",
" <td>1.0</td>\n",
" <td>3.818183</td>\n",
" <td>0.126241</td>\n",
" <td>7.921370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-1.047062</td>\n",
" <td>1.0</td>\n",
" <td>1.634985</td>\n",
" <td>0.200352</td>\n",
" <td>4.991215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>-0.684863</td>\n",
" <td>1.0</td>\n",
" <td>4.796301</td>\n",
" <td>0.304386</td>\n",
" <td>3.285307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>-2.093958</td>\n",
" <td>1.0</td>\n",
" <td>1.872111</td>\n",
" <td>0.047618</td>\n",
" <td>21.000604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>-0.759673</td>\n",
" <td>1.0</td>\n",
" <td>3.602334</td>\n",
" <td>0.280563</td>\n",
" <td>3.564258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.088109</td>\n",
" <td>1.0</td>\n",
" <td>3.036698</td>\n",
" <td>0.589879</td>\n",
" <td>1.695262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>-0.088266</td>\n",
" <td>1.0</td>\n",
" <td>5.417690</td>\n",
" <td>0.522970</td>\n",
" <td>1.912157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.732897</td>\n",
" <td>1.0</td>\n",
" <td>4.492788</td>\n",
" <td>0.795125</td>\n",
" <td>1.257663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>-0.219486</td>\n",
" <td>1.0</td>\n",
" <td>5.343016</td>\n",
" <td>0.472511</td>\n",
" <td>2.116351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2.141338</td>\n",
" <td>1.0</td>\n",
" <td>8.467091</td>\n",
" <td>0.971373</td>\n",
" <td>1.029471</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-0.882449</td>\n",
" <td>1.0</td>\n",
" <td>3.798317</td>\n",
" <td>0.244036</td>\n",
" <td>4.097761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>-1.607051</td>\n",
" <td>1.0</td>\n",
" <td>2.544114</td>\n",
" <td>0.095679</td>\n",
" <td>10.451596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-1.161911</td>\n",
" <td>1.0</td>\n",
" <td>3.738860</td>\n",
" <td>0.173517</td>\n",
" <td>5.763117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-0.231020</td>\n",
" <td>1.0</td>\n",
" <td>4.498442</td>\n",
" <td>0.468088</td>\n",
" <td>2.136352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.039060</td>\n",
" <td>1.0</td>\n",
" <td>6.023652</td>\n",
" <td>0.571496</td>\n",
" <td>1.749793</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>-1.475004</td>\n",
" <td>1.0</td>\n",
" <td>3.181862</td>\n",
" <td>0.114772</td>\n",
" <td>8.712944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>-1.245270</td>\n",
" <td>1.0</td>\n",
" <td>3.452331</td>\n",
" <td>0.155877</td>\n",
" <td>6.415317</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-1.462970</td>\n",
" <td>1.0</td>\n",
" <td>2.789579</td>\n",
" <td>0.116667</td>\n",
" <td>8.571375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>-0.747720</td>\n",
" <td>1.0</td>\n",
" <td>5.979018</td>\n",
" <td>0.284293</td>\n",
" <td>3.517502</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.204794</td>\n",
" <td>1.0</td>\n",
" <td>4.298358</td>\n",
" <td>0.632533</td>\n",
" <td>1.580944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-1.462970</td>\n",
" <td>1.0</td>\n",
" <td>2.789579</td>\n",
" <td>0.116667</td>\n",
" <td>8.571375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>970</th>\n",
" <td>0.585389</td>\n",
" <td>1.0</td>\n",
" <td>4.770087</td>\n",
" <td>0.755657</td>\n",
" <td>1.323351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>971</th>\n",
" <td>-0.463985</td>\n",
" <td>1.0</td>\n",
" <td>4.360344</td>\n",
" <td>0.380726</td>\n",
" <td>2.626557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>972</th>\n",
" <td>-0.000277</td>\n",
" <td>1.0</td>\n",
" <td>6.186571</td>\n",
" <td>0.556606</td>\n",
" <td>1.796602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>973</th>\n",
" <td>-0.565264</td>\n",
" <td>1.0</td>\n",
" <td>5.140859</td>\n",
" <td>0.344708</td>\n",
" <td>2.901003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>974</th>\n",
" <td>0.300746</td>\n",
" <td>1.0</td>\n",
" <td>6.617655</td>\n",
" <td>0.666149</td>\n",
" <td>1.501166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>975</th>\n",
" <td>-1.737922</td>\n",
" <td>1.0</td>\n",
" <td>3.137234</td>\n",
" <td>0.079610</td>\n",
" <td>12.561213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>976</th>\n",
" <td>-1.475004</td>\n",
" <td>1.0</td>\n",
" <td>3.181862</td>\n",
" <td>0.114772</td>\n",
" <td>8.712944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>977</th>\n",
" <td>-0.312912</td>\n",
" <td>1.0</td>\n",
" <td>5.526235</td>\n",
" <td>0.436866</td>\n",
" <td>2.289034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>978</th>\n",
" <td>-0.099804</td>\n",
" <td>1.0</td>\n",
" <td>3.285228</td>\n",
" <td>0.518537</td>\n",
" <td>1.928504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>979</th>\n",
" <td>-0.313551</td>\n",
" <td>1.0</td>\n",
" <td>6.051264</td>\n",
" <td>0.436623</td>\n",
" <td>2.290303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>980</th>\n",
" <td>0.218947</td>\n",
" <td>1.0</td>\n",
" <td>6.080291</td>\n",
" <td>0.637583</td>\n",
" <td>1.568423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>981</th>\n",
" <td>0.377573</td>\n",
" <td>1.0</td>\n",
" <td>3.604044</td>\n",
" <td>0.691915</td>\n",
" <td>1.445263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>982</th>\n",
" <td>-1.161911</td>\n",
" <td>1.0</td>\n",
" <td>3.738860</td>\n",
" <td>0.173517</td>\n",
" <td>5.763117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>983</th>\n",
" <td>-1.849992</td>\n",
" <td>1.0</td>\n",
" <td>3.393936</td>\n",
" <td>0.067851</td>\n",
" <td>14.738253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>984</th>\n",
" <td>0.149097</td>\n",
" <td>1.0</td>\n",
" <td>5.979875</td>\n",
" <td>0.612387</td>\n",
" <td>1.632955</td>\n",
" </tr>\n",
" <tr>\n",
" <th>985</th>\n",
" <td>-2.093958</td>\n",
" <td>1.0</td>\n",
" <td>1.872111</td>\n",
" <td>0.047618</td>\n",
" <td>21.000604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>986</th>\n",
" <td>-1.607051</td>\n",
" <td>1.0</td>\n",
" <td>2.544114</td>\n",
" <td>0.095679</td>\n",
" <td>10.451596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>987</th>\n",
" <td>-0.791433</td>\n",
" <td>1.0</td>\n",
" <td>3.832149</td>\n",
" <td>0.270801</td>\n",
" <td>3.692747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>988</th>\n",
" <td>-0.483879</td>\n",
" <td>1.0</td>\n",
" <td>5.664137</td>\n",
" <td>0.373533</td>\n",
" <td>2.677142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>989</th>\n",
" <td>-0.660901</td>\n",
" <td>1.0</td>\n",
" <td>4.405076</td>\n",
" <td>0.312252</td>\n",
" <td>3.202542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>990</th>\n",
" <td>-1.125713</td>\n",
" <td>1.0</td>\n",
" <td>4.324655</td>\n",
" <td>0.181655</td>\n",
" <td>5.504951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>991</th>\n",
" <td>-2.212717</td>\n",
" <td>1.0</td>\n",
" <td>2.434425</td>\n",
" <td>0.039979</td>\n",
" <td>25.012842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>992</th>\n",
" <td>-0.709882</td>\n",
" <td>1.0</td>\n",
" <td>6.162809</td>\n",
" <td>0.296292</td>\n",
" <td>3.375048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>993</th>\n",
" <td>-2.093958</td>\n",
" <td>1.0</td>\n",
" <td>1.872111</td>\n",
" <td>0.047618</td>\n",
" <td>21.000604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>994</th>\n",
" <td>-0.992078</td>\n",
" <td>1.0</td>\n",
" <td>4.016608</td>\n",
" <td>0.214258</td>\n",
" <td>4.667278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>0.112935</td>\n",
" <td>1.0</td>\n",
" <td>4.269773</td>\n",
" <td>0.599093</td>\n",
" <td>1.669191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>-0.759673</td>\n",
" <td>1.0</td>\n",
" <td>3.602334</td>\n",
" <td>0.280563</td>\n",
" <td>3.564258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>0.218947</td>\n",
" <td>1.0</td>\n",
" <td>6.080291</td>\n",
" <td>0.637583</td>\n",
" <td>1.568423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>-1.535155</td>\n",
" <td>1.0</td>\n",
" <td>2.576505</td>\n",
" <td>0.105694</td>\n",
" <td>9.461285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>-0.645237</td>\n",
" <td>1.0</td>\n",
" <td>5.077819</td>\n",
" <td>0.317454</td>\n",
" <td>3.150066</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" X0 v y propensity_score weight\n",
"0 1.059672 1.0 5.719181 0.865199 1.155803\n",
"1 -1.161911 1.0 3.738860 0.173517 5.763117\n",
"2 -0.720072 1.0 4.821778 0.293032 3.412596\n",
"3 -1.369444 1.0 3.066326 0.132344 7.556092\n",
"4 -0.372525 1.0 5.355411 0.414434 2.412929\n",
"5 -1.405906 1.0 1.364267 0.126030 7.934625\n",
"6 -0.133946 1.0 5.715764 0.505405 1.978611\n",
"7 -0.924519 1.0 3.134797 0.232287 4.305027\n",
"8 -1.950544 1.0 3.323188 0.058691 17.038313\n",
"9 -1.404664 1.0 3.818183 0.126241 7.921370\n",
"10 -1.047062 1.0 1.634985 0.200352 4.991215\n",
"11 -0.684863 1.0 4.796301 0.304386 3.285307\n",
"12 -2.093958 1.0 1.872111 0.047618 21.000604\n",
"13 -0.759673 1.0 3.602334 0.280563 3.564258\n",
"14 0.088109 1.0 3.036698 0.589879 1.695262\n",
"15 -0.088266 1.0 5.417690 0.522970 1.912157\n",
"16 0.732897 1.0 4.492788 0.795125 1.257663\n",
"17 -0.219486 1.0 5.343016 0.472511 2.116351\n",
"18 2.141338 1.0 8.467091 0.971373 1.029471\n",
"19 -0.882449 1.0 3.798317 0.244036 4.097761\n",
"20 -1.607051 1.0 2.544114 0.095679 10.451596\n",
"21 -1.161911 1.0 3.738860 0.173517 5.763117\n",
"22 -0.231020 1.0 4.498442 0.468088 2.136352\n",
"23 0.039060 1.0 6.023652 0.571496 1.749793\n",
"24 -1.475004 1.0 3.181862 0.114772 8.712944\n",
"25 -1.245270 1.0 3.452331 0.155877 6.415317\n",
"26 -1.462970 1.0 2.789579 0.116667 8.571375\n",
"27 -0.747720 1.0 5.979018 0.284293 3.517502\n",
"28 0.204794 1.0 4.298358 0.632533 1.580944\n",
"29 -1.462970 1.0 2.789579 0.116667 8.571375\n",
".. ... ... ... ... ...\n",
"970 0.585389 1.0 4.770087 0.755657 1.323351\n",
"971 -0.463985 1.0 4.360344 0.380726 2.626557\n",
"972 -0.000277 1.0 6.186571 0.556606 1.796602\n",
"973 -0.565264 1.0 5.140859 0.344708 2.901003\n",
"974 0.300746 1.0 6.617655 0.666149 1.501166\n",
"975 -1.737922 1.0 3.137234 0.079610 12.561213\n",
"976 -1.475004 1.0 3.181862 0.114772 8.712944\n",
"977 -0.312912 1.0 5.526235 0.436866 2.289034\n",
"978 -0.099804 1.0 3.285228 0.518537 1.928504\n",
"979 -0.313551 1.0 6.051264 0.436623 2.290303\n",
"980 0.218947 1.0 6.080291 0.637583 1.568423\n",
"981 0.377573 1.0 3.604044 0.691915 1.445263\n",
"982 -1.161911 1.0 3.738860 0.173517 5.763117\n",
"983 -1.849992 1.0 3.393936 0.067851 14.738253\n",
"984 0.149097 1.0 5.979875 0.612387 1.632955\n",
"985 -2.093958 1.0 1.872111 0.047618 21.000604\n",
"986 -1.607051 1.0 2.544114 0.095679 10.451596\n",
"987 -0.791433 1.0 3.832149 0.270801 3.692747\n",
"988 -0.483879 1.0 5.664137 0.373533 2.677142\n",
"989 -0.660901 1.0 4.405076 0.312252 3.202542\n",
"990 -1.125713 1.0 4.324655 0.181655 5.504951\n",
"991 -2.212717 1.0 2.434425 0.039979 25.012842\n",
"992 -0.709882 1.0 6.162809 0.296292 3.375048\n",
"993 -2.093958 1.0 1.872111 0.047618 21.000604\n",
"994 -0.992078 1.0 4.016608 0.214258 4.667278\n",
"995 0.112935 1.0 4.269773 0.599093 1.669191\n",
"996 -0.759673 1.0 3.602334 0.280563 3.564258\n",
"997 0.218947 1.0 6.080291 0.637583 1.568423\n",
"998 -1.535155 1.0 2.576505 0.105694 9.461285\n",
"999 -0.645237 1.0 5.077819 0.317454 3.150066\n",
"\n",
"[1000 rows x 5 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf_1"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>X0</th>\n",
" <th>v</th>\n",
" <th>y</th>\n",
" <th>propensity_score</th>\n",
" <th>weight</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>1.940774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>-0.014672</td>\n",
" <td>0.0</td>\n",
" <td>-1.239185</td>\n",
" <td>0.551130</td>\n",
" <td>2.227819</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>-1.031212</td>\n",
" <td>0.0</td>\n",
" <td>0.140711</td>\n",
" <td>0.204290</td>\n",
" <td>1.256739</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>-1.032067</td>\n",
" <td>0.0</td>\n",
" <td>-1.850035</td>\n",
" <td>0.204076</td>\n",
" <td>1.256401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>-0.447390</td>\n",
" <td>0.0</td>\n",
" <td>0.091274</td>\n",
" <td>0.386768</td>\n",
" <td>1.630705</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>-1.580430</td>\n",
" <td>0.0</td>\n",
" <td>-2.089343</td>\n",
" <td>0.099284</td>\n",
" <td>1.110228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>0.281453</td>\n",
" <td>0.0</td>\n",
" <td>1.043265</td>\n",
" <td>0.659511</td>\n",
" <td>2.936951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>-1.197542</td>\n",
" <td>0.0</td>\n",
" <td>-2.319876</td>\n",
" <td>0.165791</td>\n",
" <td>1.198740</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>-0.765560</td>\n",
" <td>0.0</td>\n",
" <td>-1.668542</td>\n",
" <td>0.278738</td>\n",
" <td>1.386458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>-2.830199</td>\n",
" <td>0.0</td>\n",
" <td>-4.803378</td>\n",
" <td>0.015841</td>\n",
" <td>1.016096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>10</td>\n",
" <td>-0.079929</td>\n",
" <td>0.0</td>\n",
" <td>-0.105781</td>\n",
" <td>0.526171</td>\n",
" <td>2.110464</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>11</td>\n",
" <td>-2.159873</td>\n",
" <td>0.0</td>\n",
" <td>-3.197416</td>\n",
" <td>0.043221</td>\n",
" <td>1.045174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>12</td>\n",
" <td>-2.230548</td>\n",
" <td>0.0</td>\n",
" <td>-2.750035</td>\n",
" <td>0.038939</td>\n",
" <td>1.040517</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>13</td>\n",
" <td>-1.210708</td>\n",
" <td>0.0</td>\n",
" <td>-0.382182</td>\n",
" <td>0.163007</td>\n",
" <td>1.194753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>14</td>\n",
" <td>-0.118687</td>\n",
" <td>0.0</td>\n",
" <td>1.638510</td>\n",
" <td>0.511276</td>\n",
" <td>2.046145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>15</td>\n",
" <td>1.135281</td>\n",
" <td>0.0</td>\n",
" <td>-0.276102</td>\n",
" <td>0.878207</td>\n",
" <td>8.210658</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>16</td>\n",
" <td>0.241850</td>\n",
" <td>0.0</td>\n",
" <td>1.804202</td>\n",
" <td>0.645690</td>\n",
" <td>2.822387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>17</td>\n",
" <td>0.592212</td>\n",
" <td>0.0</td>\n",
" <td>0.788882</td>\n",
" <td>0.757592</td>\n",
" <td>4.125268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>18</td>\n",
" <td>-1.118731</td>\n",
" <td>0.0</td>\n",
" <td>-1.095691</td>\n",
" <td>0.183258</td>\n",
" <td>1.224377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>19</td>\n",
" <td>-0.692231</td>\n",
" <td>0.0</td>\n",
" <td>-0.235574</td>\n",
" <td>0.301989</td>\n",
" <td>1.432643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>20</td>\n",
" <td>0.251081</td>\n",
" <td>0.0</td>\n",
" <td>2.306411</td>\n",
" <td>0.648934</td>\n",
" <td>2.848470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>21</td>\n",
" <td>-0.203798</td>\n",
" <td>0.0</td>\n",
" <td>0.105714</td>\n",
" <td>0.478535</td>\n",
" <td>1.917673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>22</td>\n",
" <td>0.192048</td>\n",
" <td>0.0</td>\n",
" <td>-1.286848</td>\n",
" <td>0.627961</td>\n",
" <td>2.687891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>23</td>\n",
" <td>0.314712</td>\n",
" <td>0.0</td>\n",
" <td>1.461244</td>\n",
" <td>0.670913</td>\n",
" <td>3.038709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>24</td>\n",
" <td>-0.713200</td>\n",
" <td>0.0</td>\n",
" <td>-0.223229</td>\n",
" <td>0.295228</td>\n",
" <td>1.418899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>25</td>\n",
" <td>-1.870198</td>\n",
" <td>0.0</td>\n",
" <td>-1.083580</td>\n",
" <td>0.065909</td>\n",
" <td>1.070560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>26</td>\n",
" <td>-0.914892</td>\n",
" <td>0.0</td>\n",
" <td>-2.576357</td>\n",
" <td>0.234940</td>\n",
" <td>1.307087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>27</td>\n",
" <td>-1.021966</td>\n",
" <td>0.0</td>\n",
" <td>-0.222469</td>\n",
" <td>0.206613</td>\n",
" <td>1.260419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>28</td>\n",
" <td>1.135281</td>\n",
" <td>0.0</td>\n",
" <td>-0.276102</td>\n",
" <td>0.878207</td>\n",
" <td>8.210658</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>29</td>\n",
" <td>-0.816041</td>\n",
" <td>0.0</td>\n",
" <td>-0.437849</td>\n",
" <td>0.263385</td>\n",
" <td>1.357562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>970</th>\n",
" <td>970</td>\n",
" <td>0.243437</td>\n",
" <td>0.0</td>\n",
" <td>0.713106</td>\n",
" <td>0.646249</td>\n",
" <td>2.826846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>971</th>\n",
" <td>971</td>\n",
" <td>-0.562112</td>\n",
" <td>0.0</td>\n",
" <td>0.981296</td>\n",
" <td>0.345805</td>\n",
" <td>1.528597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>972</th>\n",
" <td>972</td>\n",
" <td>-1.759704</td>\n",
" <td>0.0</td>\n",
" <td>-1.926540</td>\n",
" <td>0.077188</td>\n",
" <td>1.083644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>973</th>\n",
" <td>973</td>\n",
" <td>0.367733</td>\n",
" <td>0.0</td>\n",
" <td>-1.362551</td>\n",
" <td>0.688677</td>\n",
" <td>3.212097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>974</th>\n",
" <td>974</td>\n",
" <td>0.457542</td>\n",
" <td>0.0</td>\n",
" <td>-0.033124</td>\n",
" <td>0.717521</td>\n",
" <td>3.540091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>975</th>\n",
" <td>975</td>\n",
" <td>-0.588538</td>\n",
" <td>0.0</td>\n",
" <td>-0.093206</td>\n",
" <td>0.336661</td>\n",
" <td>1.507524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>976</th>\n",
" <td>976</td>\n",
" <td>0.448100</td>\n",
" <td>0.0</td>\n",
" <td>2.359522</td>\n",
" <td>0.714566</td>\n",
" <td>3.503436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>977</th>\n",
" <td>977</td>\n",
" <td>1.025322</td>\n",
" <td>0.0</td>\n",
" <td>1.386912</td>\n",
" <td>0.858912</td>\n",
" <td>7.087761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>978</th>\n",
" <td>978</td>\n",
" <td>-1.903113</td>\n",
" <td>0.0</td>\n",
" <td>0.184110</td>\n",
" <td>0.062858</td>\n",
" <td>1.067074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>979</th>\n",
" <td>979</td>\n",
" <td>-0.174916</td>\n",
" <td>0.0</td>\n",
" <td>-0.816614</td>\n",
" <td>0.489638</td>\n",
" <td>1.959395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>980</th>\n",
" <td>980</td>\n",
" <td>-0.776052</td>\n",
" <td>0.0</td>\n",
" <td>-0.217315</td>\n",
" <td>0.275502</td>\n",
" <td>1.380266</td>\n",
" </tr>\n",
" <tr>\n",
" <th>981</th>\n",
" <td>981</td>\n",
" <td>-0.107787</td>\n",
" <td>0.0</td>\n",
" <td>0.199005</td>\n",
" <td>0.515468</td>\n",
" <td>2.063847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>982</th>\n",
" <td>982</td>\n",
" <td>-0.056032</td>\n",
" <td>0.0</td>\n",
" <td>-0.528141</td>\n",
" <td>0.535333</td>\n",
" <td>2.152078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>983</th>\n",
" <td>983</td>\n",
" <td>-0.191714</td>\n",
" <td>0.0</td>\n",
" <td>0.210425</td>\n",
" <td>0.483178</td>\n",
" <td>1.934904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>984</th>\n",
" <td>984</td>\n",
" <td>-1.378189</td>\n",
" <td>0.0</td>\n",
" <td>-2.404268</td>\n",
" <td>0.130805</td>\n",
" <td>1.150490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>985</th>\n",
" <td>985</td>\n",
" <td>-2.207825</td>\n",
" <td>0.0</td>\n",
" <td>-2.901097</td>\n",
" <td>0.040269</td>\n",
" <td>1.041959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>986</th>\n",
" <td>986</td>\n",
" <td>-1.706450</td>\n",
" <td>0.0</td>\n",
" <td>-2.685407</td>\n",
" <td>0.083233</td>\n",
" <td>1.090790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>987</th>\n",
" <td>987</td>\n",
" <td>-2.161688</td>\n",
" <td>0.0</td>\n",
" <td>-2.765515</td>\n",
" <td>0.043106</td>\n",
" <td>1.045048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>988</th>\n",
" <td>988</td>\n",
" <td>-1.217770</td>\n",
" <td>0.0</td>\n",
" <td>-0.813250</td>\n",
" <td>0.161529</td>\n",
" <td>1.192647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>989</th>\n",
" <td>989</td>\n",
" <td>-0.482159</td>\n",
" <td>0.0</td>\n",
" <td>-1.201947</td>\n",
" <td>0.374152</td>\n",
" <td>1.597833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>990</th>\n",
" <td>990</td>\n",
" <td>0.612730</td>\n",
" <td>0.0</td>\n",
" <td>0.101522</td>\n",
" <td>0.763345</td>\n",
" <td>4.225564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>991</th>\n",
" <td>991</td>\n",
" <td>-1.227540</td>\n",
" <td>0.0</td>\n",
" <td>-1.715798</td>\n",
" <td>0.159502</td>\n",
" <td>1.189771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>992</th>\n",
" <td>992</td>\n",
" <td>-1.460442</td>\n",
" <td>0.0</td>\n",
" <td>-3.192458</td>\n",
" <td>0.117069</td>\n",
" <td>1.132591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>993</th>\n",
" <td>993</td>\n",
" <td>-0.732332</td>\n",
" <td>0.0</td>\n",
" <td>-0.767871</td>\n",
" <td>0.289137</td>\n",
" <td>1.406741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>994</th>\n",
" <td>994</td>\n",
" <td>-2.336816</td>\n",
" <td>0.0</td>\n",
" <td>-3.766734</td>\n",
" <td>0.033258</td>\n",
" <td>1.034402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>995</td>\n",
" <td>-1.050889</td>\n",
" <td>0.0</td>\n",
" <td>-1.054832</td>\n",
" <td>0.199410</td>\n",
" <td>1.249078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>996</td>\n",
" <td>0.457542</td>\n",
" <td>0.0</td>\n",
" <td>-0.033124</td>\n",
" <td>0.717521</td>\n",
" <td>3.540091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>997</td>\n",
" <td>-0.916193</td>\n",
" <td>0.0</td>\n",
" <td>-0.514720</td>\n",
" <td>0.234580</td>\n",
" <td>1.306473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>998</td>\n",
" <td>0.206950</td>\n",
" <td>0.0</td>\n",
" <td>0.715253</td>\n",
" <td>0.633305</td>\n",
" <td>2.727060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>999</td>\n",
" <td>0.174847</td>\n",
" <td>0.0</td>\n",
" <td>1.638787</td>\n",
" <td>0.621754</td>\n",
" <td>2.643780</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" index X0 v y propensity_score weight\n",
"0 0 -0.187648 0.0 -2.339453 0.484742 1.940774\n",
"1 1 -0.014672 0.0 -1.239185 0.551130 2.227819\n",
"2 2 -1.031212 0.0 0.140711 0.204290 1.256739\n",
"3 3 -1.032067 0.0 -1.850035 0.204076 1.256401\n",
"4 4 -0.447390 0.0 0.091274 0.386768 1.630705\n",
"5 5 -1.580430 0.0 -2.089343 0.099284 1.110228\n",
"6 6 0.281453 0.0 1.043265 0.659511 2.936951\n",
"7 7 -1.197542 0.0 -2.319876 0.165791 1.198740\n",
"8 8 -0.765560 0.0 -1.668542 0.278738 1.386458\n",
"9 9 -2.830199 0.0 -4.803378 0.015841 1.016096\n",
"10 10 -0.079929 0.0 -0.105781 0.526171 2.110464\n",
"11 11 -2.159873 0.0 -3.197416 0.043221 1.045174\n",
"12 12 -2.230548 0.0 -2.750035 0.038939 1.040517\n",
"13 13 -1.210708 0.0 -0.382182 0.163007 1.194753\n",
"14 14 -0.118687 0.0 1.638510 0.511276 2.046145\n",
"15 15 1.135281 0.0 -0.276102 0.878207 8.210658\n",
"16 16 0.241850 0.0 1.804202 0.645690 2.822387\n",
"17 17 0.592212 0.0 0.788882 0.757592 4.125268\n",
"18 18 -1.118731 0.0 -1.095691 0.183258 1.224377\n",
"19 19 -0.692231 0.0 -0.235574 0.301989 1.432643\n",
"20 20 0.251081 0.0 2.306411 0.648934 2.848470\n",
"21 21 -0.203798 0.0 0.105714 0.478535 1.917673\n",
"22 22 0.192048 0.0 -1.286848 0.627961 2.687891\n",
"23 23 0.314712 0.0 1.461244 0.670913 3.038709\n",
"24 24 -0.713200 0.0 -0.223229 0.295228 1.418899\n",
"25 25 -1.870198 0.0 -1.083580 0.065909 1.070560\n",
"26 26 -0.914892 0.0 -2.576357 0.234940 1.307087\n",
"27 27 -1.021966 0.0 -0.222469 0.206613 1.260419\n",
"28 28 1.135281 0.0 -0.276102 0.878207 8.210658\n",
"29 29 -0.816041 0.0 -0.437849 0.263385 1.357562\n",
".. ... ... ... ... ... ...\n",
"970 970 0.243437 0.0 0.713106 0.646249 2.826846\n",
"971 971 -0.562112 0.0 0.981296 0.345805 1.528597\n",
"972 972 -1.759704 0.0 -1.926540 0.077188 1.083644\n",
"973 973 0.367733 0.0 -1.362551 0.688677 3.212097\n",
"974 974 0.457542 0.0 -0.033124 0.717521 3.540091\n",
"975 975 -0.588538 0.0 -0.093206 0.336661 1.507524\n",
"976 976 0.448100 0.0 2.359522 0.714566 3.503436\n",
"977 977 1.025322 0.0 1.386912 0.858912 7.087761\n",
"978 978 -1.903113 0.0 0.184110 0.062858 1.067074\n",
"979 979 -0.174916 0.0 -0.816614 0.489638 1.959395\n",
"980 980 -0.776052 0.0 -0.217315 0.275502 1.380266\n",
"981 981 -0.107787 0.0 0.199005 0.515468 2.063847\n",
"982 982 -0.056032 0.0 -0.528141 0.535333 2.152078\n",
"983 983 -0.191714 0.0 0.210425 0.483178 1.934904\n",
"984 984 -1.378189 0.0 -2.404268 0.130805 1.150490\n",
"985 985 -2.207825 0.0 -2.901097 0.040269 1.041959\n",
"986 986 -1.706450 0.0 -2.685407 0.083233 1.090790\n",
"987 987 -2.161688 0.0 -2.765515 0.043106 1.045048\n",
"988 988 -1.217770 0.0 -0.813250 0.161529 1.192647\n",
"989 989 -0.482159 0.0 -1.201947 0.374152 1.597833\n",
"990 990 0.612730 0.0 0.101522 0.763345 4.225564\n",
"991 991 -1.227540 0.0 -1.715798 0.159502 1.189771\n",
"992 992 -1.460442 0.0 -3.192458 0.117069 1.132591\n",
"993 993 -0.732332 0.0 -0.767871 0.289137 1.406741\n",
"994 994 -2.336816 0.0 -3.766734 0.033258 1.034402\n",
"995 995 -1.050889 0.0 -1.054832 0.199410 1.249078\n",
"996 996 0.457542 0.0 -0.033124 0.717521 3.540091\n",
"997 997 -0.916193 0.0 -0.514720 0.234580 1.306473\n",
"998 998 0.206950 0.0 0.715253 0.633305 2.727060\n",
"999 999 0.174847 0.0 1.638787 0.621754 2.643780\n",
"\n",
"[1000 rows x 6 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cdf_0.reset_index()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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