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Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
Tinbergen Institute Discussion Paper No. TI 2021-001/V
Verlag: 
Tinbergen Institute, Amsterdam and Rotterdam
Zusammenfassung: 
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.
Schlagwörter: 
Machine learning
causal inference
average treatment effects
heterogeneous treatment effects
JEL: 
D04
C01
C21
Dokumentart: 
Working Paper
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