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Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection

Author

Listed:
  • Advani, Arun

    (University of Warwick, CAGE, and Institute for Fiscal Studies)

  • Kitagawa, Toru

    (University College London and cemmap)

  • Sloczynski, Tymon

    (Brandeis University and IZA)

Abstract
We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to help select a treatment effect estimator under unconfoundedness. We show theoretically that neither is likely to be informative except under restrictive conditions that are unlikely to be satisfied in many contexts. To test empirical relevance, we also apply the approaches to a real-world setting where estimator performance is known. Both approaches are worse than random at selecting estimators which minimise absolute bias. They are better when selecting estimators that minimise mean squared error. However, using a simple bootstrap is at least as good and often better. For now researchers would be best advised to use a range of estimators and compare estimates for robustness.

Suggested Citation

  • Advani, Arun & Kitagawa, Toru & Sloczynski, Tymon, 2019. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," CAGE Online Working Paper Series 411, Competitive Advantage in the Global Economy (CAGE).
  • Handle: RePEc:cge:wacage:411
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    2. Harsh Parikh & Carlos Varjao & Louise Xu & Eric Tchetgen Tchetgen, 2022. "Validating Causal Inference Methods," Papers 2202.04208, arXiv.org, revised Jul 2022.
    3. Tymon Sloczynski & S. Derya Uysal & Jeffrey M. Wooldridge & Derya Uysal, 2022. "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect," CESifo Working Paper Series 9715, CESifo.
    4. Athey, Susan & Imbens, Guido W. & Metzger, Jonas & Munro, Evan, 2024. "Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 240(2).
    5. Arun Advani & Toru Kitagawa & Tymon Słoczyński, 2019. "Mostly harmless simulations? Using Monte Carlo studies for estimator selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 893-910, September.
    6. Lombardi, Stefano & van den Berg, Gerard J. & Vikström, Johan, 2020. "Empirical Monte Carlo evidence on estimation of Timing-of-Events models," Working Paper Series 2020:26, IFAU - Institute for Evaluation of Labour Market and Education Policy, revised 05 Jan 2021.
    7. Tymon Sloczynski & S. Derya Uysal & Jeffrey M. Wooldridge & Derya Uysal, 2022. "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect," CESifo Working Paper Series 9715, CESifo.
    8. Yiyan Huang & Cheuk Hang Leung & Siyi Wang & Yijun Li & Qi Wu, 2024. "Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators," Papers 2402.18392, arXiv.org, revised Oct 2024.
    9. Cummins Joseph & Miller Douglas L. & Smith Brock & Simon David, 2024. "Matching on Noise: Finite Sample Bias in the Synthetic Control Estimator," Journal of Econometric Methods, De Gruyter, vol. 13(1), pages 67-95, January.
    10. Florian Gunsilius, 2019. "A path-sampling method to partially identify causal effects in instrumental variable models," Papers 1910.09502, arXiv.org, revised Jun 2020.

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    More about this item

    Keywords

    empirical Monte Carlo studies; program evaluation; selection on observables; treatment effects JEL Classification: C15; C21; C25; C52;
    All these keywords.

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing

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