Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/229707
Authors: 
Baiardi, Anna
Naghi, Andrea A.
Year of Publication: 
2021
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2021-001/V
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.
Subjects: 
Machine learning
causal inference
average treatment effects
heterogeneous treatment effects
JEL: 
D04
C01
C21
Document Type: 
Working Paper

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