Causal Effect Inference with Deep Latent-Variable Models

TL;DR

This paper introduces a deep latent-variable model using Variational Autoencoders to robustly infer individual causal effects from observational data, even with noisy confounder proxies.

Contribution

It proposes a novel VAE-based approach that estimates latent confounders and causal effects simultaneously, improving robustness over existing methods.

Findings

Outperforms existing methods on benchmark datasets.

Effectively handles noisy confounder proxies.

Matches state-of-the-art accuracy in individual treatment effect estimation.

Abstract

Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational study attempts to measure all important confounders. However, even if one does not have direct access to all confounders, there may exist noisy and uncertain measurement of proxies for confounders. We build on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect. Our method is based on Variational Autoencoders (VAE) which follow the causal structure of inference with proxies. We show our method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.