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

Author

Listed:
  • Anna Baiardi

    (Erasmus University Rotterdam)

  • Andrea A. Naghi

    (Erasmus University Rotterdam)

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.

Suggested Citation

  • Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Tinbergen Institute Discussion Papers 21-001/V, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20210001
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    References listed on IDEAS

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    Cited by:

    1. Paul Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Sep 2024.
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    3. Netta Barak‐Corren & Yoav Kan‐Tor & Nelson Tebbe, 2022. "Examining the effects of antidiscrimination laws on children in the foster care and adoption systems," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 19(4), pages 1003-1066, December.
    4. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.

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    More about this item

    Keywords

    Machine learning; causal inference; average treatment effects; heterogeneous treatment effects;
    All these keywords.

    JEL classification:

    • D04 - Microeconomics - - General - - - Microeconomic Policy: Formulation; Implementation; Evaluation
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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