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

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  • Susan Athey
  • Guido W. Imbens
  • Jonas Metzger
  • Evan M. Munro

Abstract

When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credibility of such Monte Carlo studies is often limited because of the freedom the researcher has in choosing the design. In recent years a new class of generative models emerged in the machine learning literature, termed Generative Adversarial Networks (GANs) that can be used to systematically generate artificial data that closely mimics real economic datasets, while limiting the degrees of freedom for the researcher and optionally satisfying privacy guarantees with respect to their training data. In addition if an applied researcher is concerned with the performance of a particular statistical method on a specific data set (beyond its theoretical properties in large samples), she may wish to assess the performance, e.g., the coverage rate of confidence intervals or the bias of the estimator, using simulated data which resembles her setting. Tol illustrate these methods we apply Wasserstein GANs (WGANs) to compare a number of different estimators for average treatment effects under unconfoundedness in three distinct settings (corresponding to three real data sets) and present a methodology for assessing the robustness of the results. In this example, we find that (i) there is not one estimator that outperforms the others in all three settings, so researchers should tailor their analytic approach to a given setting, and (ii) systematic simulation studies can be helpful for selecting among competing methods in this situation.

Suggested Citation

  • Susan Athey & Guido W. Imbens & Jonas Metzger & Evan M. Munro, 2019. "Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations," NBER Working Papers 26566, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:26566
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    Cited by:

    1. Chen, Jiafeng & Chen, Xiaohong & Tamer, Elie, 2023. "Efficient estimation of average derivatives in NPIV models: Simulation comparisons of neural network estimators," Journal of Econometrics, Elsevier, vol. 235(2), pages 1848-1875.
    2. Jiafeng Chen & Xiaohong Chen & Elie Tamer, 2021. "Efficient Estimation of Average Derivatives in NPIV Models: Simulation Comparisons of Neural Network Estimators," Cowles Foundation Discussion Papers 2319, Cowles Foundation for Research in Economics, Yale University.
    3. Jiaying Gu & Roger Koenker, 2023. "Invidious Comparisons: Ranking and Selection as Compound Decisions," Econometrica, Econometric Society, vol. 91(1), pages 1-41, January.
    4. Jesus Fernandez-Villaverde, 2020. "Simple Rules for a Complex World with Arti?cial Intelligence," PIER Working Paper Archive 20-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    5. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
    6. Christian M. Dahl & Emil N. S{o}rensen, 2021. "Time Series (re)sampling using Generative Adversarial Networks," Papers 2102.00208, arXiv.org.
    7. Jiafeng Chen & Xiaohong Chen & Elie Tamer, 2021. "Efficient Estimation in NPIV Models: A Comparison of Various Neural Networks-Based Estimators," Papers 2110.06763, arXiv.org, revised Oct 2022.
    8. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
    9. Yves-C'edric Bauwelinckx & Jan Dhaene & Tim Verdonck & Milan van den Heuvel, 2023. "On the causality-preservation capabilities of generative modelling," Papers 2301.01109, arXiv.org.
    10. Allison Koenecke & Hal Varian, 2020. "Synthetic Data Generation for Economists," Papers 2011.01374, arXiv.org, revised Nov 2020.
    11. Tengyuan Liang, 2020. "How Well Generative Adversarial Networks Learn Distributions," Working Papers 2020-154, Becker Friedman Institute for Research In Economics.
    12. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2021. "Deep Neural Networks for Estimation and Inference," Econometrica, Econometric Society, vol. 89(1), pages 181-213, January.
    13. Jesús Fernández-Villaverde, 2021. "Has machine learning rendered simple rules obsolete?," European Journal of Law and Economics, Springer, vol. 52(2), pages 251-265, December.
    14. Christian M. Dahl & Torben S. D. Johansen & Emil N. S{o}rensen & Christian E. Westermann & Simon F. Wittrock, 2021. "Applications of Machine Learning in Document Digitisation," Papers 2102.03239, arXiv.org.
    15. Jiaying Gu & Roger Koenker, 2020. "Invidious Comparisons: Ranking and Selection as Compound Decisions," Papers 2012.12550, arXiv.org, revised Sep 2021.

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    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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