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

Anna Baiardi and Andrea A. Naghi
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Anna Baiardi: Erasmus University Rotterdam
Andrea A. Naghi: Erasmus University Rotterdam

No 21-001/V, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: A new and rapidly growing econometric literature is making advances in the problem of using machine learning (ML) methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods and identify several advantages of using these techniques. We show that these advantages and their implications are empirically relevant and that the use of these methods can improve the credibility of causal analysis.

Keywords: Machine learning; causal inference; average treatment effects; heterogeneous treatment effects (search for similar items in EconPapers)
JEL-codes: C01 C21 D04 (search for similar items in EconPapers)
Date: 2021-01-04
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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Citations: View citations in EconPapers (4)

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