Causal ML Book

An introduction to the emerging fusion of modern statistical (machine learning) inference and causal inference methods.

The book introduces ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs)

Book

Applied Causal Inference Powered by ML and AI

Preface

Core Material

Predictive Inference 1

Linear Regression in Moderately High Dimensions

Causal Inference 1

Randomized Experiments

Predictive Inference 2A

Modern High Dimensional Linear Regression

Causal Inference 2A

Causal Identification via Conditional Exogeneity

Predictive Inference 2B

Inference on Predictive and Causal Effects in High Dimensional Linear Regression Models

Causal Inference 2B

Structural Equations and Conditional Exogeneity

Predictive Inference 3

Modern High Dimensional Nonlinear Regression

Causal Inference 3

Causal DAGs, Markov Nets, Nonlinear SEMs

Predictive Inference 4

Debiased ML Inference on Predictive and Causal Effects in Modern Nonlinear Regression Models

Predictive Inference 5

Feature Engineering With Deep Learning for Causal and Predictive Inference

Advanced Core Material

Advanced Core 1

Unobserved Confounders, Instrumental Variables, and Proxy Controls

Advanced Core 2

Debiased ML for IV and Proxy Controls Models and Robust DML Inference under Weak Identification

Topics

Topics 1

Inference on Heterogeneous Treatment Effects

Citation

This is how you can cite us

Chernozhuko, V. & Hansen, C. & Spindler, M. & Syrgkanis, V. (2022): Applied Causal Inference Powered by ML and AI

Labs

Take a look at our Labs

DoubleML

Double / debiased machine learning

The Python and R package DoubleML provide an implementation of the double / debiased machine learning framework of Chernozhukov et al. (2018). The Python package is built on top of scikit-learn (Pedregosa et al., 2011) and the R package on top of mlr3 and the mlr3 ecosystem (Lang et al., 2019).

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