InfoCEVAE: Treatment Effect Estimation with Hidden Confounding Variables Matching

Abstract

Treatment effect estimation is a fundamental problem in various domains for effective decision making. While many studies assume that observational data include all the confounding variables, we cannot practically guarantee that observational data include such confounding variables, and there might be confounding variables that are not included in observational data, referred to as hidden confounding variables. Recently, variational autencoder (VAE) based methods have been successfully applied to treatment effect estimation problem. However, although they can recover a large class of latent variable models, they do not give the correct treatment effect, even when they achieve an optimal solution due to the nature of VAE loss function. We propose an efficient VAE-based method that employs information theory to estimate treatment effect and combines it with a matching technique. To the best of our knowledge, this is the first work that gives the correct treatment effect given an optimal solution using VAE-based methods. Experiments on a semi-real dataset and synthetic dataset demonstrate that the proposed method mitigates VAE problems and observational bias effectively, even under hidden confounding variables, and outperforms strong baseline methods.