Before revision:  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 .
After revision:  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 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 .