The Value Added of Machine Learning to Causal Inference: Evidence from\n Revisited Studies

2021/01/04 by Anna Baiardi, Andrea A. Naghi, Baiardi, Anna +1 · 1 voice · 5 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Bayesian Modeling and Causal Inference #Forecasting Techniques and Applications #Monetary Policy and Economic Impact #econ.GN

paper · pdf · doi:10.48550/arxiv.2101.00878

openalex publication_date 2021/01/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A new and rapidly growing econometric literature is making advances in the\nproblem of using machine learning methods for causal inference questions. Yet,\nthe empirical economics literature has not started to fully exploit the\nstrengths of these modern methods. We revisit influential empirical studies\nwith causal machine learning methods and identify several advantages of using\nthese techniques. We show that these advantages and their implications are\nempirically relevant and that the use of these methods can improve the\ncredibility of causal analysis.\n

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Discussions

• Interesting new paper revisiting influential studies in economics using recent causal ML methods. doi.org/10.48550/arX... [bsky, 16 points, 0 comments]

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