Applied Causal Inference Powered by ML and AI

Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis
3/4/2024
econ.EMcs.LGstat.ME

No official code found. Best implementation: CausalAIBook/MetricsMLNotebooks (157 stars, MIT, last commit 2026-04-02).

Abstract

An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools.

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Code Implementations(1)

CausalAIBook/MetricsMLNotebooksCommunity100%
MIT

Notebooks for Applied Causal Inference Powered by ML and AI

15772R, HTMLFeb 24, 20216 months agoMIT
causal-inferenceeconometrics

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Cite this paper

@article{chernozhukov2024applied,
  title  = {Applied Causal Inference Powered by ML and AI},
  author = {Victor Chernozhukov and Christian Hansen and Nathan Kallus and Martin Spindler and Vasilis Syrgkanis},
  year   = {2024},
  eprint = {2403.02467},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.02467v1}
}

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