Applied Causal Inference Powered by ML and AI

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

arXiv 4 Mar 2024 · Econometrics · 36 citations (OpenAlex)

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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Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

	Citing paper	Intensity	Mentions	Sections
1	What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness	1.000	7	4
2	Dimension Reduction for Conditional Density Estimation with Applications to High-Dimensional Causal Inference	0.737	3	2
3	Causal Graphs for Conditional Parallel Trends	0.737	3	2
4	Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions	0.644	2	2
5	Double Machine Learning meets Panel Data - Promises, Pitfalls, and Potential Solutions	0.644	2	2
6	Heterogeneity Analysis with Heterogeneous Treatments	0.644	2	2
7	Refining the Notion of No Anticipation in Difference-in-Differences Studies	0.644	2	2
8	Reevaluating Causal Estimation Methods with Data from a Product Release	0.644	2	2
9	Causal Identification in Multi-Task Demand Learning with Confounding	0.644	2	2
10	Shrinkage-Based Regressions with Many Related Treatments	0.511	2	1