Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations☆
Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations☆
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DOI：
10.1016/j.jeconom.2020.09.013
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发表时间：
2024-03-18
影响因子：
6.3
通讯作者：
Munro, Evan
中科院分区：
文献类型：
--
作者：
Athey, Susan;Imbens, Guido W.;Munro, Evan
当研究人员开发新的计量经济学方法时，通常的做法是将新方法的性能与蒙特卡洛研究中现有方法的性能进行比较。由于研究人员在选择蒙特卡罗设计报告时的自由裁量权，这种蒙特卡罗研究的可信度通常是有限的。为了提高可信度，我们建议使用机器学习文献中最近开发的一类生成模型，称为生成对抗网络（GAN），可用于系统地生成密切模仿现有数据集的人工数据。因此，结合现有的真实的数据集，GAN可用于限制研究人员在蒙特卡洛研究设计中的自由度，使任何比较都更具说服力。此外，如果应用研究人员关注特定统计方法在特定数据集上的性能（超出其在大样本中的理论属性），她可以使用这种GAN来评估所提出的方法的性能，例如置信区间的覆盖率或估计量的偏差，使用与感兴趣的确切设置非常相似的模拟数据。为了说明这些方法，我们将Wasserstein GAN（WGAN）应用于估计平均治疗效果。在这个例子中，我们发现（i）没有一个估计量在所有三种设置中都优于其他估计量，因此研究人员应该根据给定的设置调整他们的分析方法，（ii）系统的模拟研究有助于在这种情况下选择竞争方法，以及（iii）生成的数据与实际数据非常相似。(c)2021爱思唯尔有限公司版权所有。
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 discretion the researcher has in choosing the Monte Carlo designs reported. To improve the credibility we propose using a class of generative models that has recently been developed in the machine learning literature, termed Generative Adversarial Networks (GANs) which can be used to systematically generate artificial data that closely mimics existing datasets. Thus, in combination with existing real data sets, GANs can be used to limit the degrees of freedom in Monte Carlo study designs for the researcher, making any comparisons more convincing. 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 can use such GANs to assess the performance of the proposed method, e.g. the coverage rate of confidence intervals or the bias of the estimator, using simulated data which closely resembles the exact setting of interest. To illustrate these methods we apply Wasserstein GANs (WGANs) to the estimation of average treatment effects. In this example, we find that ( i ) there is not a single estimator that outperforms the others in all three settings, so researchers should tailor their analytic approach to a given setting, ( ii ) systematic simulation studies can be helpful for selecting among competing methods in this situation, and ( iii ) the generated data closely resemble the actual data. (c) 2021 Elsevier B.V. All rights reserved.