# An Adversarial Approach to Structural Estimation

arXiv: https://arxiv.org/abs/2007.06169 · HTML: https://mlsanity.com/paper/2007.06169/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [Proximal Causal Learning of Conditional Average Treatment Effects](https://mlsanity.com/paper/2301.10913/) (2023)
- [Approximate Bayesian Computation via Classification](https://mlsanity.com/paper/2111.11507/) (2021)
- [Adversarial Estimators](https://mlsanity.com/paper/2204.10495/) (2022)
- [Taking a Moment for Distributional Robustness](https://mlsanity.com/paper/2405.05461/) (2024)
- [Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference](https://mlsanity.com/paper/2210.07154/) (2022)
- [Pre-Training Estimators for Structural Models: Application to Consumer Search](https://mlsanity.com/paper/2505.00526/) (2025)
- [Distributional Evaluation of Generative Models via Relative Density Ratio](https://mlsanity.com/paper/2510.25507/) (2025)
- [Generative Predictive Distributions for Time Series](https://mlsanity.com/paper/2606.16773/) (2026)

## Abstract (arXiv)

We propose a new simulation-based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates simulated observations using the structural model) and a discriminator (which classifies whether an observation is simulated). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator, the adversarial estimator attains parametric efficiency under correct specification and the parametric rate under misspecification. We advocate the use of a neural network as a discriminator that can exploit adaptivity properties and attain fast rates of convergence. We apply our method to the elderly's saving decision model and show that our estimator uncovers the bequest motive as an important source of saving across the wealth distribution, not only for the rich.