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Mostly harmless simulations? Using Monte Carlo studies for estimator selection

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  • Arun Advani
  • Toru Kitagawa
  • Tymon Słoczyński

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 that minimize absolute bias. They are better when selecting estimators that minimize 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

  • 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.
  • Handle: RePEc:wly:japmet:v:34:y:2019:i:6:p:893-910
    DOI: 10.1002/jae.2724
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    3. Hugo Bodory & Lorenzo Camponovo & Martin Huber & Michael Lechner, 2020. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 183-200, January.
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    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.
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    Citations

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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. Susan Athey & Guido Imbens & Jonas Metzger & Evan Munro, 2019. "Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations," Papers 1909.02210, arXiv.org, revised Jul 2020.
    3. 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.
    4. 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.
    5. 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

    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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