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akelleh/causal_data_frame_3.ipynb

Created January 25, 2019 03:49
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causal_data_frame_3.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": "stdout",
"output_type": "stream",
"text": [
"Error: Pygraphviz cannot be loaded. No module named 'pygraphviz'\n",
"Trying pydot ...\n",
"['X1', 'X3', 'X0', 'Z1', 'Z0', 'X2', 'X4', 'U']\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",
"yes\n",
"{'observed': 'yes'}\n",
"yes\n",
"{'observed': 'yes'}\n",
"no\n",
"{'label': '\"Unobserved Confounders\"', 'observed': 'no'}\n",
"Model to find the causal effect of treatment v on outcome y\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\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"
]
},
{
"data": {
"image/png": 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\n",
"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', dot_graph=data['dot_graph'], kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data['df'].causal.mean(x='v', y='y', common_causes=['X0', 'X1', 'X2', 'X3', 'X4'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error: Pygraphviz cannot be loaded. No module named 'pygraphviz'\n",
"Trying pydot ...\n",
"['X1', 'X3', 'X0', 'Z1', 'Z0', 'X2', 'X4', 'U']\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",
"yes\n",
"{'observed': 'yes'}\n",
"yes\n",
"{'observed': 'yes'}\n",
"no\n",
"{'label': '\"Unobserved Confounders\"', 'observed': 'no'}\n",
"Model to find the causal effect of treatment v on outcome y\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\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",
" graph=data[\"dot_graph\"])\n",
"\n",
"identified_estimand = model.identify_effect()\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error: Pygraphviz cannot be loaded. No module named 'pygraphviz'\n",
"Trying pydot ...\n",
"['X1', 'X3', 'X0', 'Z1', 'Z0', 'X2', 'X4', 'U']\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",
"yes\n",
"{'observed': 'yes'}\n",
"yes\n",
"{'observed': 'yes'}\n",
"no\n",
"{'label': '\"Unobserved Confounders\"', 'observed': 'no'}\n",
"Model to find the causal effect of treatment v on outcome y\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\n",
"{'observed': 'yes'}\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"
]
},
{
"data": {
"image/png": 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\n",
"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', dot_graph=data['dot_graph'], kind='bar')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": []
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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