Arbeitspapier
The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies
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.
- Language
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Englisch
- Bibliographic citation
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Series: Tinbergen Institute Discussion Paper ; No. TI 2021-001/V
- Classification
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Wirtschaft
Microeconomic Policy: Formulation, Implementation, and Evaluation
Econometrics
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- Subject
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Machine learning
causal inference
average treatment effects
heterogeneous treatment effects
- Event
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Geistige Schöpfung
- (who)
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Baiardi, Anna
Naghi, Andrea A.
- Event
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Veröffentlichung
- (who)
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Tinbergen Institute
- (where)
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Amsterdam and Rotterdam
- (when)
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2021
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Baiardi, Anna
- Naghi, Andrea A.
- Tinbergen Institute
Time of origin
- 2021
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