Variational Auto-Encoder Architectures that Excel at Causal Inference

TL;DR

This paper introduces a series of Variational Auto-Encoder models designed to improve causal inference from observational data, effectively handling confounding factors and outperforming existing methods.

Contribution

It presents a novel progressive modeling framework culminating in a hybrid VAE model specifically optimized for causal effect estimation.

Findings

All proposed models outperform state-of-the-art methods.

Models effectively learn underlying factors and causal effects.

Hybrid model achieves the best performance.

Abstract

Estimating causal effects from observational data (at either an individual -- or a population -- level) is critical for making many types of decisions. One approach to address this task is to learn decomposed representations of the underlying factors of data; this becomes significantly more challenging when there are confounding factors (which influence both the cause and the effect). In this paper, we take a generative approach that builds on the recent advances in Variational Auto-Encoders to simultaneously learn those underlying factors as well as the causal effects. We propose a progressive sequence of models, where each improves over the previous one, culminating in the Hybrid model. Our empirical results demonstrate that the performance of all three proposed models are superior to both state-of-the-art discriminative as well as other generative approaches in the literature.