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

arXiv: https://arxiv.org/abs/1909.02210 · HTML: https://mlsanity.com/paper/1909.02210/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 30 of 32)

- [Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data](https://mlsanity.com/paper/2310.16524/) (2023)
- [In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2302.02923/) (2023)
- [Validating Causal Inference Methods](https://mlsanity.com/paper/2202.04208/) (2022)
- [End-to-End Balancing for Causal Continuous Treatment-Effect Estimation](https://mlsanity.com/paper/2107.13068/) (2021)
- [Marginal Causal Flows for Validation and Inference](https://mlsanity.com/paper/2411.01295/) (2024)
- [Proximal Causal Learning of Conditional Average Treatment Effects](https://mlsanity.com/paper/2301.10913/) (2023)
- [Generative Bayesian Inference with GANs](https://mlsanity.com/paper/2208.12113/) (2022)
- [The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation](https://mlsanity.com/paper/2303.01412/) (2023)
- [Constrained Adversarial Learning for Automated Software Testing: a literature review](https://mlsanity.com/paper/2303.07546/) (2023)
- [Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption](https://mlsanity.com/paper/2508.08883/) (2025)
- [Efficient Estimation in NPIV Models: A Comparison of Various Neural Networks-Based Estimators](https://mlsanity.com/paper/2110.06763/) (2021)
- [Time Series (re)sampling using Generative Adversarial Networks](https://mlsanity.com/paper/2102.00208/) (2021)
- [Double Robust Bayesian Inference on Average Treatment Effects](https://mlsanity.com/paper/2211.16298/) (2022)
- [Estimating Wage Disparities Using Foundation Models](https://mlsanity.com/paper/2409.09894/) (2024)
- [Unpacking the Black Box: Regulating Algorithmic Decisions](https://mlsanity.com/paper/2110.03443/) (2021)
- [Learning to Simulate: Generative Metamodeling via Quantile Regression](https://mlsanity.com/paper/2311.17797/) (2023)
- [Applications of Machine Learning in Document Digitisation](https://mlsanity.com/paper/2102.03239/) (2021)
- [Undersmoothing Causal Estimators with Generative Trees](https://mlsanity.com/paper/2203.08570/) (2022)
- [Adversarial Estimators](https://mlsanity.com/paper/2204.10495/) (2022)
- [Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators](https://mlsanity.com/paper/2402.18392/) (2024)
- [Time-Series Foundation AI Model for Value-at-Risk Forecasting](https://mlsanity.com/paper/2410.11773/) (2024)
- [Frugal, Flexible, Faithful: Causal Data Simulation via Frengression](https://mlsanity.com/paper/2508.01018/) (2025)
- [Downstream Fairness Caveats with Synthetic Healthcare Data](https://mlsanity.com/paper/2203.04462/) (2022)
- [On the causality-preservation capabilities of generative modelling](https://mlsanity.com/paper/2301.01109/) (2023)
- [Generative Conditional Distributions by Neural (Entropic) Optimal Transport](https://mlsanity.com/paper/2406.02317/) (2024)
- [Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance](https://mlsanity.com/paper/2506.15305/) (2025)
- [Robust Orthogonal Machine Learning of Treatment Effects](https://mlsanity.com/paper/2103.11869/) (2021)
- [From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding](https://mlsanity.com/paper/2409.15503/) (2024)
- ["Over-optimizing" for Normality: Budget-constrained Uncertainty Quantification for Contextual Decision-making](https://mlsanity.com/paper/2503.12747/) (2025)
- [Improving Generative Methods for Causal Evaluation via Simulation-Based Inference](https://mlsanity.com/paper/2509.02892/) (2025)

## Abstract (arXiv)

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.