CausalEGM: a general causal inference framework by encoding generative modeling

TL;DR

CausalEGM introduces a novel framework that leverages generative modeling to estimate causal effects in high-dimensional confounder settings, applicable to both binary and continuous treatments, with theoretical guarantees and superior empirical performance.

Contribution

It develops a general encoding generative model framework for causal inference that handles high-dimensional confounders and provides theoretical and empirical validation.

Findings

Outperforms existing methods in high-dimensional confounder scenarios

Provides theoretical bounds on excess risk and consistency guarantees

Demonstrates superior empirical performance in various experiments

Abstract

Although understanding and characterizing causal effects have become essential in observational studies, it is challenging when the confounders are high-dimensional. In this article, we develop a general framework $\textit{CausalEGM}$ for estimating causal effects by encoding generative modeling, which can be applied in both binary and continuous treatment settings. Under the potential outcome framework with unconfoundedness, we establish a bidirectional transformation between the high-dimensional confounders space and a low-dimensional latent space where the density is known (e.g., multivariate normal distribution). Through this, CausalEGM simultaneously decouples the dependencies of confounders on both treatment and outcome and maps the confounders to the low-dimensional latent space. By conditioning on the low-dimensional latent features, CausalEGM can estimate the causal effect for each individual or the average causal effect within a population. Our theoretical analysis shows that the excess risk for CausalEGM can be bounded through empirical process theory. Under an assumption on encoder-decoder networks, the consistency of the estimate can be guaranteed. In a series of experiments, CausalEGM demonstrates superior performance over existing methods for both binary and continuous treatments. Specifically, we find CausalEGM to be substantially more powerful than competing methods in the presence of large sample sizes and high dimensional confounders. The software of CausalEGM is freely available at https://github.com/SUwonglab/CausalEGM.