# Applied Causal Inference

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Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis

An introduction to the emerging fusion of machine learning and causal inference.

The book introduces ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and presents Debiased Machine Learning methods to do inference in such models using modern predictive tools.

Causal ML Book™

## Applied Causal Inference Powered by ML and AI©

V. Chernozhukov, C. Hansen, N. Kallus, M. Spindler, V. Syrgkanis

## Core Material

## Advanced Topics

## Endorsements

Citation

## This is how you can cite us

Labs

## Take a look at our Labs

DOUBLEML, ECONML, STATA ML, ddml

## Packages

DoubleML

## Double / Debiased Machine Learning

The Python and R packages 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).

EconML

## ML Estimation of Heterogeneous Treatment Effects

The Python package EconML provides an implementation of a variety of methods for estimating heterogeneous treatment effects with machine learning.

Stata ML & ddml

## Stata Machine Learning & Double/Debiased ML for R

- The Stata ML Page features packages tailored for machine learning in Stata, focusing on prediction, model selection, and causal inference, enhancing Stata's functionality with cutting-edge statistical methods.
- ddml is an implementation for R of double/debiased machine learning estimators as proposed by Chernozhukov et al. (2018).