This book bridges the gap between machine learning and causal inference, providing rigorous methods for answering causal questions using modern ML tools. Topics span predictive inference, causal identification, double/debiased machine learning, heterogeneous treatment effects, instrumental variables, difference-in-differences, regression discontinuity, and more.
Read the full book and download individual chapters at CausalML-Book.org
All chapters are available for free at causalml-book.org .
| Chapter |
| P |
Preface |
| 0 |
Powering Causal Inference with ML and AI |
| Chapter |
Topics |
| 1 |
Predictive Inference with Linear Regression in Moderately High Dimensions |
Prediction Inference |
| 2 |
Causal Inference via Randomized Experiments |
Causality Inference |
| 3 |
Predictive Inference via Modern High-Dimensional Linear Regression |
Prediction |
| 4 |
Statistical Inference on Predictive Effects in High-Dimensional Linear Regression Models |
Causality Inference |
| 5 |
Causal Inference via Conditional Ignorability |
Causality |
| 6 |
Causal Inference via Linear Structural Equations |
Causality |
| 7 |
Causal Inference via DAGs and Nonlinear Structural Equation Models |
Causality |
| 8 |
Predictive Inference via Modern Nonlinear Regression |
Prediction |
| 9 |
Statistical Inference on Predictive and Causal Effects in Modern Nonlinear Regression Models |
Causality Inference |
| 10 |
Feature Engineering for Causal and Predictive Inference |
Causality Inference |
| Chapter |
| 11 |
Deeper Dive into DAGs, Good and Bad Controls |
| 12 |
Unobserved Confounders, Instrumental Variables, and Proxy Controls |
| 13 |
DML for IV and Proxy Controls Models and Robust DML Inference under Weak Identification |
| 14 |
Statistical Inference on Heterogeneous Treatment Effects |
| 15 |
Estimation and Validation of Heterogeneous Treatment Effects |
| 16 |
Difference-in-Differences |
| 17 |
Regression Discontinuity Designs |
Ch 1 — Prediction & Linear Regression
Ch 2 — Randomized Experiments
Ch 3 — High-Dimensional Linear Regression
| Lab |
Python |
R |
| Penalized Linear Regressions: Simulation |
Colab |
Colab |
| Case Study: Wage Prediction with ML |
Colab |
Colab |
Ch 4 — Inference in High-Dimensional Models
| Lab |
Python |
R |
| Simulation on Orthogonal Estimation |
Colab |
Colab |
| Comparing Orthogonal vs Non-Orthogonal Methods |
Colab |
Colab |
| Testing the Convergence Hypothesis |
Colab |
Colab |
| Heterogeneous Effect of Sex on Wage |
Colab |
Colab |
Ch 6–7 — DAGs & Structural Equations
Ch 8 — Nonlinear Prediction
| Lab |
Python |
R |
| ML Estimators for Wage Prediction |
Colab |
Colab |
| Functional Approximations by Trees and Neural Nets |
Colab |
Colab |
Ch 9 — DML for Causal & Predictive Effects
| Lab |
Python |
R |
| Effect of Gun Ownership on Homicide |
Colab |
Colab |
| DAG Analysis of 401(k) Impact |
Colab |
Colab |
| DML Inference on 401(k) Wealth Effects |
Colab |
Colab |
| DML for Partially Linear Model (Growth) |
Colab |
Colab |
Ch 10 — Feature Engineering
| Lab |
Python |
R |
| Variational Autoencoders and PCA |
Colab |
Colab |
| DoubleML Feature Engineering with BERT |
Colab |
— |
Ch 12–13 — IV, Proxy Controls & Weak Identification
Ch 14–16 — Heterogeneous Effects & Diff-in-Diff
| Lab |
Python |
R |
| CATE Estimation with Causal Forests |
Colab |
— |
| CATE Inference: Best Linear Predictors |
Colab |
— |
| Conditional Average Treatment Effects |
Colab |
— |
| Difference-in-Differences: Minimum Wage |
Colab |
Colab |
Companion open-source implementations for the methods covered in the book.
@article{chernozhukov2024applied,
title = {Applied Causal Inference Powered by ML and AI},
author = {Chernozhukov, Victor and Hansen, Christian and Kallus, Nathan
and Spindler, Martin and Syrgkanis, Vasilis},
journal = {arXiv preprint arXiv:2403.02467},
year = {2024},
doi = {10.48550/arXiv.2403.02467}
}