Generative Causal Inference

TL;DR

This paper introduces generative Bayesian methods for causal inference that use simulation and optimal transport to estimate posterior distributions without MCMC, applicable in diverse econometric settings.

Contribution

It proposes Quantile ReLU networks for density-free inference and demonstrates a novel generative approach for causal inference with nonlinearity and heterogeneity.

Findings

Effective in modeling complex causal relationships

Avoids MCMC by using supervised learning and optimal transport

Applicable to various econometric data generating processes

Abstract

Generative Bayesian Computation (GBC) methods are developed for Casual Inference. Generative methods are simulation-based methods that use a large training dataset to represent posterior distributions as a map (a.k.a. optimal transport) to a base distribution. They avoid the use of MCMC by replacing the conditional posterior inference problem with a supervised learning problem. We further propose the use Quantile ReLU networks which are density free and hence apply in a variety of Econometric settings where data generating processes are specified by deterministic latent variables updates or as moment constraints. Generative approaches directly simulate large samples of observables and unobservable (parameters, latent variables) and then apply high-dimensional quantile regression to learn a nonlinear transport map from base distribution to parameter inference. We illustrate our methodology in the field of causal inference. Our approach can also handle nonlinearity and heterogeneity. Finally, we conclude with the directions for future research.