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

Citations

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Cited by:

  1. Leon Tran & Ting Ye & Peng Ding & Fang Han, 2026. "Generative modeling for the bootstrap," Papers 2602.17052, arXiv.org.
  2. Tengyuan Liang, 2020. "How Well Generative Adversarial Networks Learn Distributions," Working Papers 2020-154, Becker Friedman Institute for Research In Economics.
  3. 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.
  4. Aditya Ghosh & Guido Imbens & Stefan Wager, 2025. "PLRD: Partially Linear Regression Discontinuity Inference," Papers 2503.09907, arXiv.org, revised Nov 2025.
  5. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Apr 2026.
  6. Jiaying Gu & Roger Koenker, 2023. "Invidious Comparisons: Ranking and Selection as Compound Decisions," Econometrica, Econometric Society, vol. 91(1), pages 1-41, January.
  7. Ruonan Xu & Xiye Yang, 2025. "Distributionally Robust Treatment Effect," Papers 2512.12781, arXiv.org, revised Apr 2026.
  8. Lin, Zhexiao & Han, Fang, 2025. "On regression-adjusted imputation estimators of average treatment effects," Journal of Econometrics, Elsevier, vol. 251(C).
  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. 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.
  11. Nir Billfeld & Moshe Kim, 2024. "Context-dependent Causality (the Non-Nonotonic Case)," Papers 2404.05021, arXiv.org.
  12. Richard H. Spady & Sami Stouli, 2025. "Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions," Econometrica, Econometric Society, vol. 93(5), pages 1885-1913, September.
  13. 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.
  14. Allison Koenecke & Hal Varian, 2020. "Synthetic Data Generation for Economists," Papers 2011.01374, arXiv.org, revised Nov 2020.
  15. 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.
  16. Yanqin Fan & Yuan Qi & Gaoqian Xu, 2025. "Policy Learning with $\alpha$-Expected Welfare," Papers 2505.00256, arXiv.org.
  17. Christian M. Dahl & Emil N. S{o}rensen, 2021. "Time Series (re)sampling using Generative Adversarial Networks," Papers 2102.00208, arXiv.org.
  18. Alfred Galichon & Marc Henry, 2026. "An econometrician's guide to optimal transport," Papers 2604.04227, arXiv.org.
  19. Jiaying Gu & Roger Koenker, 2020. "Invidious Comparisons: Ranking and Selection as Compound Decisions," Papers 2012.12550, arXiv.org, revised Sep 2021.
  20. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
  21. 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.
  22. 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.
  23. 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.
  24. Takanori Ida & Takunori Ishihara & Koichiro Ito & Daido Kido & Toru Kitagawa & Shosei Sakaguchi & Shusaku Sasaki, 2026. "Choosing Who Chooses: Selection‐Driven Targeting in Energy Rebate Programs," Econometrica, Econometric Society, vol. 94(1), pages 225-247, January.
  25. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2025. "Double Robust Bayesian Inference on Average Treatment Effects," Econometrica, Econometric Society, vol. 93(2), pages 539-568, March.
  26. Kevin Han & Han Wu & Linjia Wu & Yu Shi & Canyao Liu, 2024. "Estimating Treatment Effects Using Observational Data and Experimental Data with Non-Overlapping Support," Econometrics, MDPI, vol. 12(3), pages 1-11, September.
  27. Jordi Llorens-Terrazas & Mika Meitz, 2026. "Generative Predictive Distributions for Time Series," Papers 2606.16773, arXiv.org.
  28. Liu, Yeshen & Song, Kaixi & Xu, Longhui & Song, Zhe, 2026. "ESG and firms’ exploitative versus exploratory innovation: Evidence from a large language model approach," Research in International Business and Finance, Elsevier, vol. 83(C).
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