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

TL;DR

This paper reviews how modern machine learning methods enhance causal inference in empirical economics, demonstrating their advantages and potential to improve the credibility of causal analysis.

Contribution

It revisits influential studies to highlight the empirical benefits and practical relevance of integrating causal machine learning methods into econometric analysis.

Findings

Machine learning improves causal inference credibility.

Modern methods offer advantages over traditional approaches.

Empirical relevance of causal machine learning is demonstrated.

Abstract

A new and rapidly growing econometric literature is making advances in the problem of using machine learning 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.