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akelleh/causal_data_frame.ipynb

Created January 25, 2019 03:18
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causal_data_frame.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from dowhy.do_why import CausalModel\n",
"import dowhy.datasets\n",
"import dowhy.api.causal_data_frame\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data = dowhy.datasets.linear_dataset(beta=10,\n",
" num_common_causes=5,\n",
" num_instruments = 2,\n",
" num_samples=1000,\n",
" treatment_is_binary=True)\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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['X2', 'X4', 'X1', 'X0', 'X3']\n",
"yes\n",
"{'observed': 'yes'}\n",
"yes\n",
"{'observed': 'yes'}\n",
"yes\n",
"{'observed': 'yes'}\n",
"yes\n",
"{'observed': 'yes'}\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",
"There are unobserved common causes. Causal effect cannot be identified.\n",
"WARN: Do you want to continue by ignoring these unobserved confounders? [y/n] yes\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"LinearRegressionEstimator\n",
"LinearRegressionEstimator\n",
"LinearRegressionEstimator\n",
"LinearRegressionEstimator\n",
"LinearRegressionEstimator\n",
"LinearRegressionEstimator\n",
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"LinearRegressionEstimator\n",
"LinearRegressionEstimator\n",
"LinearRegressionEstimator\n",
"LinearRegressionEstimator\n",
"LinearRegressionEstimator\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data['df'].causal.plot(x='v', y='y', common_causes=['X0', 'X1', 'X2', 'X3', 'X4'], kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"['X2', 'X4', 'X1', 'X0', 'X3']\n",
"yes\n",
"{'observed': 'yes'}\n",
"yes\n",
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"yes\n",
"{'observed': 'yes'}\n",
"yes\n",
"{'observed': 'yes'}\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",
"There are unobserved common causes. Causal effect cannot be identified.\n",
"WARN: Do you want to continue by ignoring these unobserved confounders? [y/n] yes\n",
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"LinearRegressionEstimator\n",
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"LinearRegressionEstimator\n",
"LinearRegressionEstimator\n",
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},
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"text/plain": [
" Z0 Z1 X0 X1 X2 X3 X4 \\\n",
"v \n",
"0.0 0.000000 0.490464 -0.554550 -0.011503 0.143462 -0.159936 -0.635517 \n",
"1.0 0.016238 0.517577 0.177178 0.165350 0.991146 0.348743 0.340302 \n",
"\n",
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"v \n",
"0.0 -3.695264 2.28321 \n",
"1.0 14.394687 12.28321 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['df'].causal.mean(x='v', y='y', common_causes=['X0', 'X1', 'X2', 'X3', 'X4'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['X2', 'X4', 'X1', 'X0', 'X3']\n",
"yes\n",
"{'observed': 'yes'}\n",
"yes\n",
"{'observed': 'yes'}\n",
"yes\n",
"{'observed': 'yes'}\n",
"yes\n",
"{'observed': 'yes'}\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",
"There are unobserved common causes. Causal effect cannot be identified.\n",
"WARN: Do you want to continue by ignoring these unobserved confounders? [y/n] yes\n"
]
}
],
"source": [
"model= CausalModel(\n",
" data=data[\"df\"],\n",
" treatment=data[\"treatment_name\"],\n",
" outcome=data[\"outcome_name\"],\n",
" common_causes=data[\"common_causes_names\"])\n",
"\n",
"identified_estimand = model.identify_effect()\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LinearRegressionEstimator\n",
"LinearRegressionEstimator\n"
]
},
{
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],
"source": [
"model.do(1., \n",
" identified_estimand=identified_estimand,\n",
" method_name=\"backdoor.linear_regression\") - \\\n",
"model.do(0., \n",
" identified_estimand=identified_estimand,\n",
" method_name=\"backdoor.linear_regression\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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