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Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations

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  • Susan Athey
  • Guido Imbens
  • Jonas Metzger
  • Evan Munro

Abstract

Researchers often use artificial data to assess the performance of new econometric methods. In many cases the data generating processes used in these Monte Carlo studies do not resemble real data sets and instead reflect many arbitrary decisions made by the researchers. As a result potential users of the methods are rarely persuaded by these simulations that the new methods are as attractive as the simulations make them out to be. We discuss the use of Wasserstein Generative Adversarial Networks (WGANs) as a method for systematically generating artificial data that mimic closely any given real data set without the researcher having many degrees of freedom. We apply the methods to compare in three different settings twelve different estimators for average treatment effects under unconfoundedness. We conclude in this example that (i) there is not one estimator that outperforms the others in all three settings, and (ii) that systematic simulation studies can be helpful for selecting among competing methods.

Suggested Citation

  • 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.
  • Handle: RePEc:arx:papers:1909.02210
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    File URL: http://arxiv.org/pdf/1909.02210
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    References listed on IDEAS

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    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    2. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    3. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    4. Abadie, Alberto & Imbens, Guido W., 2011. "Bias-Corrected Matching Estimators for Average Treatment Effects," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 1-11.
    5. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    6. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    7. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
    8. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    9. Arun Advani & Toru Kitagawa & Tymon S{l}oczy'nski, 2018. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," Papers 1809.09527, arXiv.org, revised Apr 2019.
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