added lalonde ipw estimate
This commit is contained in:
Amit Sharma
Родитель
b1c8362c1a
Коммит
8a5722ea34
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# DoWhy example on the Lalonde dataset\n",
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"\n",
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"Thanks to [@mizuy](https://github.com/mizuy) for providing this example. Here we use the Lalonde dataset and apply IPW estimator to it. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/lib/python3.5/dist-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: Installing package into ‘/home/amit/R/x86_64-pc-linux-gnu-library/3.4’\n",
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"(as ‘lib’ is unspecified)\n",
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"\n",
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" warnings.warn(x, RRuntimeWarning)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The rpy2.ipython extension is already loaded. To reload it, use:\n",
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" %reload_ext rpy2.ipython\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/lib/python3.5/dist-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: trying URL 'https://cloud.r-project.org/src/contrib/Matching_4.9-3.tar.gz'\n",
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"\n",
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" warnings.warn(x, RRuntimeWarning)\n",
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"/usr/local/lib/python3.5/dist-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: Content type 'application/x-gzip'\n",
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" warnings.warn(x, RRuntimeWarning)\n",
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"/usr/local/lib/python3.5/dist-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: length 302135 bytes (295 KB)\n",
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"\n",
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" warnings.warn(x, RRuntimeWarning)\n",
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"/usr/local/lib/python3.5/dist-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: =\n",
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" warnings.warn(x, RRuntimeWarning)\n",
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"/usr/local/lib/python3.5/dist-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: \n",
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"\n",
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" warnings.warn(x, RRuntimeWarning)\n",
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"/usr/local/lib/python3.5/dist-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: downloaded 295 KB\n",
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"\n",
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"\n",
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" warnings.warn(x, RRuntimeWarning)\n",
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"/usr/local/lib/python3.5/dist-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: \n",
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" warnings.warn(x, RRuntimeWarning)\n",
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"/usr/local/lib/python3.5/dist-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: The downloaded source packages are in\n",
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"\t‘/tmp/RtmpVW644g/downloaded_packages’\n",
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" warnings.warn(x, RRuntimeWarning)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"array(['Matching', 'MASS', 'tools', 'stats', 'graphics', 'grDevices',\n",
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" 'utils', 'datasets', 'methods', 'base'], \n",
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" dtype='<U9')"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import os, sys\n",
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"sys.path.append(os.path.abspath(\"../../\"))\n",
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"\n",
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"import dowhy\n",
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"from dowhy.do_why import CausalModel\n",
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"from rpy2.robjects import r as R\n",
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"%load_ext rpy2.ipython\n",
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"\n",
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"#%R install.packages(\"Matching\")\n",
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"%R library(Matching)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Load the data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"%R data(lalonde)\n",
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"%R -o lalonde\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Run DoWhy analysis: model, identify, estimate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:dowhy.do_why:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n",
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"INFO:dowhy.causal_identifier:Common causes of treatment and outcome:{'educ', 'nodegr', 'hisp', 'married', 'black', 'age', 'U'}\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model to find the causal effect of treatment treat on outcome re78\n",
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"There are unobserved common causes. Causal effect cannot be identified.\n",
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"WARN: Do you want to continue by ignoring these unobserved confounders? [y/n] y\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"PropensityScoreWeightingEstimator\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
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"INFO:dowhy.causal_estimator:b: re78~treat+educ+nodegr+hisp+married+black+age\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Causal Estimate is 1634.98683597\n"
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]
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}
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],
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"source": [
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"\n",
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"model=CausalModel(\n",
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" data = lalonde,\n",
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" treatment='treat',\n",
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" outcome='re78',\n",
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" common_causes='nodegr+black+hisp+age+educ+married'.split('+'))\n",
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"identified_estimand = model.identify_effect()\n",
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"estimate = model.estimate_effect(identified_estimand,\n",
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" method_name=\"backdoor.propensity_score_weighting\")\n",
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"#print(estimate)\n",
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"print(\"Causal Estimate is \" + str(estimate.value))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Sanity check: compare to manual IPW estimate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Causal Estimate is 1634.98683597\n"
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]
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}
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],
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"source": [
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"df = model._data\n",
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"ps = df['ps']\n",
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"y = df['re78']\n",
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"z = df['treat']\n",
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"\n",
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"ey1 = z*y/ps / sum(z/ps)\n",
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"ey0 = (1-z)*y/(1-ps) / sum((1-z)/(1-ps))\n",
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"ate = ey1.sum()-ey0.sum()\n",
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"print(\"Causal Estimate is \" + str(ate))\n",
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"\n",
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"# correct -> Causal Estimate is 1634.9868359746906"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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