apoorvalal · s3alfisc · Jul 30, 2026 · Jul 30, 2026 · Jul 30, 2026 · Jul 30, 2026
diff --git a/pyfixest/Chapter01CorrAssocSimpsons.ipynb b/pyfixest/Chapter01CorrAssocSimpsons.ipynb
diff --git a/Chapter02PotentialOutcomes.ipynb → pyfixest/Chapter02PotentialOutcomes.ipynb b/Chapter02PotentialOutcomes.ipynb → pyfixest/Chapter02PotentialOutcomes.ipynb
diff --git a/Chapter03CREandFRT.ipynb → pyfixest/Chapter03CREandFRT.ipynb b/Chapter03CREandFRT.ipynb → pyfixest/Chapter03CREandFRT.ipynb
diff --git a/pyfixest/Chapter04CREandNeyman.ipynb b/pyfixest/Chapter04CREandNeyman.ipynb
diff --git a/pyfixest/Chapter05StratandPostStrat.ipynb b/pyfixest/Chapter05StratandPostStrat.ipynb
diff --git a/pyfixest/Chapter06RegadjRerand.ipynb b/pyfixest/Chapter06RegadjRerand.ipynb
diff --git a/utils.py → pyfixest/utils.py b/utils.py → pyfixest/utils.py
diff --git a/Chapter01CorrAssocSimpsons.ipynb → statsmodels/Chapter01CorrAssocSimpsons.ipynb b/Chapter01CorrAssocSimpsons.ipynb → statsmodels/Chapter01CorrAssocSimpsons.ipynb
diff --git a/statsmodels/Chapter02PotentialOutcomes.ipynb b/statsmodels/Chapter02PotentialOutcomes.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 2: Potential Outcomes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pandas           : 2.1.1\n",
+      "matplotlib       : 3.8.0\n",
+      "scipy            : 1.11.3\n",
+      "numpy            : 1.23.5\n",
+      "matplotlib_inline: 0.1.6\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import scipy as sp\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "\n",
+    "InteractiveShell.ast_node_interactivity = \"all\"\n",
+    "\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 1\n",
+    "\n",
+    "%load_ext watermark\n",
+    "%watermark --iversions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = 500\n",
+    "Y0 = sp.stats.norm.rvs(size=n)\n",
+    "tau = -0.5 + Y0\n",
+    "Y1 = Y0 + tau"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perfect doctor: treat if individual TE is positive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.3555878913957384"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Z = tau >= 0\n",
+    "Y = Z * Y1 + (1 - Z) * Y0\n",
+    "np.mean(Y[Z == 1]) - np.mean(Y[Z == 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Clueless doctor: flip coin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.4046654673989749"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Z = sp.stats.bernoulli.rvs(p=0.5, size=n)\n",
+    "Y = Z * Y1 + (1 - Z) * Y0\n",
+    "np.mean(Y[Z == 1]) - np.mean(Y[Z == 0])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "econometrics",
+   "language": "python",
+   "name": "econometrics"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/statsmodels/Chapter03CREandFRT.ipynb b/statsmodels/Chapter03CREandFRT.ipynb
diff --git a/Chapter04CREandNeyman.ipynb → statsmodels/Chapter04CREandNeyman.ipynb b/Chapter04CREandNeyman.ipynb → statsmodels/Chapter04CREandNeyman.ipynb
diff --git a/Chapter05StratandPostStrat.ipynb → statsmodels/Chapter05StratandPostStrat.ipynb b/Chapter05StratandPostStrat.ipynb → statsmodels/Chapter05StratandPostStrat.ipynb
diff --git a/Chapter06RegadjRerand.ipynb → statsmodels/Chapter06RegadjRerand.ipynb b/Chapter06RegadjRerand.ipynb → statsmodels/Chapter06RegadjRerand.ipynb
diff --git a/Chapter07MatchedPairs.ipynb → statsmodels/Chapter07MatchedPairs.ipynb b/Chapter07MatchedPairs.ipynb → statsmodels/Chapter07MatchedPairs.ipynb
diff --git a/Chapter08UnifyingFisherNeyman.ipynb → ...odels/Chapter08UnifyingFisherNeyman.ipynb b/Chapter08UnifyingFisherNeyman.ipynb → ...odels/Chapter08UnifyingFisherNeyman.ipynb
diff --git a/Chapter09BridgingFinitePopAndSuperPop.ipynb → ...apter09BridgingFinitePopAndSuperPop.ipynb b/Chapter09BridgingFinitePopAndSuperPop.ipynb → ...apter09BridgingFinitePopAndSuperPop.ipynb
diff --git a/Chapter10ObsStudiesSelBias.ipynb → statsmodels/Chapter10ObsStudiesSelBias.ipynb b/Chapter10ObsStudiesSelBias.ipynb → statsmodels/Chapter10ObsStudiesSelBias.ipynb
diff --git a/Chapter11Pscore.ipynb → statsmodels/Chapter11Pscore.ipynb b/Chapter11Pscore.ipynb → statsmodels/Chapter11Pscore.ipynb
diff --git a/Chapter12DoubleRobustATE.ipynb → statsmodels/Chapter12DoubleRobustATE.ipynb b/Chapter12DoubleRobustATE.ipynb → statsmodels/Chapter12DoubleRobustATE.ipynb
diff --git a/Chapter13DoubleRobustATT.ipynb → statsmodels/Chapter13DoubleRobustATT.ipynb b/Chapter13DoubleRobustATT.ipynb → statsmodels/Chapter13DoubleRobustATT.ipynb
diff --git a/Chapter15Matching.ipynb → statsmodels/Chapter15Matching.ipynb b/Chapter15Matching.ipynb → statsmodels/Chapter15Matching.ipynb
diff --git a/Chapter16UnconfDifficulties.ipynb → ...smodels/Chapter16UnconfDifficulties.ipynb b/Chapter16UnconfDifficulties.ipynb → ...smodels/Chapter16UnconfDifficulties.ipynb
diff --git a/Chapter17Evalue.ipynb → statsmodels/Chapter17Evalue.ipynb b/Chapter17Evalue.ipynb → statsmodels/Chapter17Evalue.ipynb
diff --git a/Chapter18SensitivityAnalysis.ipynb → ...models/Chapter18SensitivityAnalysis.ipynb b/Chapter18SensitivityAnalysis.ipynb → ...models/Chapter18SensitivityAnalysis.ipynb
diff --git a/Chapter19RosenbaumPvalues.ipynb → statsmodels/Chapter19RosenbaumPvalues.ipynb b/Chapter19RosenbaumPvalues.ipynb → statsmodels/Chapter19RosenbaumPvalues.ipynb
diff --git a/Chapter20OverlapRD.ipynb → statsmodels/Chapter20OverlapRD.ipynb b/Chapter20OverlapRD.ipynb → statsmodels/Chapter20OverlapRD.ipynb
diff --git a/Chapter21IVexperiments.ipynb → statsmodels/Chapter21IVexperiments.ipynb b/Chapter21IVexperiments.ipynb → statsmodels/Chapter21IVexperiments.ipynb
diff --git a/Chapter22IVmixtureDist.ipynb → statsmodels/Chapter22IVmixtureDist.ipynb b/Chapter22IVmixtureDist.ipynb → statsmodels/Chapter22IVmixtureDist.ipynb
diff --git a/Chapter23IVeconometrics.ipynb → statsmodels/Chapter23IVeconometrics.ipynb b/Chapter23IVeconometrics.ipynb → statsmodels/Chapter23IVeconometrics.ipynb
diff --git a/Chapter24IVfuzzyRD.ipynb → statsmodels/Chapter24IVfuzzyRD.ipynb b/Chapter24IVfuzzyRD.ipynb → statsmodels/Chapter24IVfuzzyRD.ipynb
diff --git a/Chapter25IVmendelian.ipynb → statsmodels/Chapter25IVmendelian.ipynb b/Chapter25IVmendelian.ipynb → statsmodels/Chapter25IVmendelian.ipynb
diff --git a/Chapter26principalStratification.ipynb → ...ls/Chapter26principalStratification.ipynb b/Chapter26principalStratification.ipynb → ...ls/Chapter26principalStratification.ipynb
diff --git a/Chapter27mediationAnalysis.ipynb → statsmodels/Chapter27mediationAnalysis.ipynb b/Chapter27mediationAnalysis.ipynb → statsmodels/Chapter27mediationAnalysis.ipynb
diff --git a/ChapterA.ipynb → statsmodels/ChapterA.ipynb b/ChapterA.ipynb → statsmodels/ChapterA.ipynb
diff --git a/statsmodels/utils.py b/statsmodels/utils.py
@@ -0,0 +1,14 @@
+import numpy as np
+import pandas as pd
+import graphviz as gr
+
+def simulate(**kwargs):
+  values = {}
+  g = gr.Digraph()
+  for k,v in kwargs.items():
+    parents = v.__code__.co_varnames
+    inputs = {arg: values[arg] for arg in v.__code__.co_varnames}
+    values[k] = v(**inputs)
+    for p in parents:
+      g.edge(p, k)
+  return pd.DataFrame(values), g