Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2021-01-04
Number: 21-001/V
Author-Name: Anna Baiardi
Author-Workplace-Name: Erasmus University Rotterdam
Author-Name: Andrea A. Naghi
Author-Workplace-Name: Erasmus University Rotterdam
Title: The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies
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.
Classification-JEL: D04, C01, C21
Keywords: Machine learning, causal inference, average treatment effects, heterogeneous treatment effects
File-URL: https://papers.tinbergen.nl/21001.pdf
File-Format: application/pdf
File-Size: 595092 bytes
Handle: RePEc:tin:wpaper:20210001