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doctor-phil committed May 20, 2024
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80 changes: 40 additions & 40 deletions _notebooks/applications/heterogeneity.ipynb
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"cells": [
{
"cell_type": "markdown",
"id": "ce0d164e",
"id": "a4fcd1e3",
"metadata": {},
"source": [
"# Heterogeneous Effects\n",
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{
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{
"cell_type": "code",
"execution_count": null,
"id": "93e20798",
"id": "cef1d0a4",
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},
{
"cell_type": "markdown",
"id": "a63e85ba",
"id": "82917e2f",
"metadata": {},
"source": [
"In this notebook, we will learn how to apply machine learning methods to analyze\n",
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},
{
"cell_type": "markdown",
"id": "e2b7c5c0",
"id": "ef16f249",
"metadata": {},
"source": [
"## Background and Data\n",
Expand All @@ -102,7 +102,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "17ab90b3",
"id": "ca6df4cf",
"metadata": {
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Expand All @@ -115,7 +115,7 @@
},
{
"cell_type": "markdown",
"id": "185c4f98",
"id": "1a27e788",
"metadata": {},
"source": [
"## Potential Outcomes and Treatment Effects\n",
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},
{
"cell_type": "markdown",
"id": "e4353765",
"id": "9f22fd97",
"metadata": {},
"source": [
"### Average Treatment Effects\n",
Expand All @@ -166,7 +166,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "e634c8b0",
"id": "f26f9757",
"metadata": {
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{
"cell_type": "code",
"execution_count": null,
"id": "87032e10",
"id": "f03e5d2f",
"metadata": {
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},
{
"cell_type": "markdown",
"id": "125cd36a",
"id": "0455867b",
"metadata": {},
"source": [
"The program did increase the percent of births assisted by a medical\n",
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},
{
"cell_type": "markdown",
"id": "59ff6f59",
"id": "af4a5645",
"metadata": {},
"source": [
"### Conditional Average Treatment Effects\n",
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},
{
"cell_type": "markdown",
"id": "108bc2db",
"id": "d8a3a81d",
"metadata": {},
"source": [
"## Generic Machine Learning Inference\n",
Expand All @@ -286,7 +286,7 @@
},
{
"cell_type": "markdown",
"id": "ea3d4d7a",
"id": "e21541d4",
"metadata": {},
"source": [
"### Best Linear Projection of CATE\n",
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},
{
"cell_type": "markdown",
"id": "28208088",
"id": "b39500d9",
"metadata": {},
"source": [
"### Grouped Average Treatment Effects\n",
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},
{
"cell_type": "markdown",
"id": "ec82917b",
"id": "a8da8640",
"metadata": {},
"source": [
"### Estimation\n",
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{
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"execution_count": null,
"id": "bbda07b4",
"id": "f6ca32c7",
"metadata": {
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{
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{
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{
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{
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{
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{
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{
"cell_type": "code",
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"metadata": {
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},
{
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"metadata": {},
"source": [
"From the second table above, we see that regardless of the machine\n",
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},
{
"cell_type": "markdown",
"id": "74d3e026",
"id": "ac83a2c1",
"metadata": {},
"source": [
"### Assisted Delivery\n",
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{
"cell_type": "code",
"execution_count": null,
"id": "357f57ff",
"id": "ba75bc97",
"metadata": {
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{
"cell_type": "code",
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"id": "cecf975c",
"id": "a6d2a871",
"metadata": {
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},
{
"cell_type": "markdown",
"id": "3467e58a",
"id": "94cd84e5",
"metadata": {},
"source": [
"Now, the results are more encouraging. For all four machine learning\n",
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},
{
"cell_type": "markdown",
"id": "0b41a2c7",
"id": "23046e84",
"metadata": {},
"source": [
"### Covariate Means by Group\n",
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{
"cell_type": "code",
"execution_count": null,
"id": "3a07d7c7",
"id": "c199015e",
"metadata": {
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{
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"metadata": {
"hide-output": false
},
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{
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"execution_count": null,
"id": "111afc55",
"id": "621b5dd1",
"metadata": {
"hide-output": false
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{
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"id": "a90c770b",
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"metadata": {
"hide-output": false
},
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{
"cell_type": "code",
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"id": "07f1484e",
"id": "1c0df257",
"metadata": {
"hide-output": false
},
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},
{
"cell_type": "markdown",
"id": "fb891f22",
"id": "6f87a3e0",
"metadata": {},
"source": [
"From this, we see that the group predicted to be most affected by treatment\n",
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},
{
"cell_type": "markdown",
"id": "b1c6e744",
"id": "e796b33d",
"metadata": {},
"source": [
"### Caution\n",
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},
{
"cell_type": "markdown",
"id": "dcf41869",
"id": "a6ec565f",
"metadata": {},
"source": [
"## Causal Trees and Forests\n",
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},
{
"cell_type": "markdown",
"id": "a824ab6c",
"id": "c3f84e43",
"metadata": {},
"source": [
"## References\n",
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}
],
"metadata": {
"date": 1712969536.8874295,
"date": 1716224502.791021,
"filename": "heterogeneity.md",
"kernelspec": {
"display_name": "Python",
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