Intact-VAE: Estimating Treatment Effects under Unobserved Confounding

TL;DR

Intact-VAE introduces a novel variational autoencoder approach to estimate treatment effects in causal inference, addressing unobserved confounding by modeling confounders as latent variables, and demonstrates strong empirical performance.

Contribution

The paper proposes Intact-VAE, a new VAE variant that leverages the prognostic score for identifying treatment effects under unobserved confounding, with theoretical guarantees and empirical validation.

Findings

Achieves state-of-the-art results on synthetic datasets

Provides theoretical identification of treatment effects

Ensures consistent estimation with balanced representations

Abstract

NOTE: This preprint has a flawed theoretical formulation. Please avoid it and refer to the ICLR22 publication https://openreview.net/forum?id=q7n2RngwOM. Also, arXiv:2109.15062 contains some new ideas on unobserved Confounding. As an important problem of causal inference, we discuss the identification and estimation of treatment effects under unobserved confounding. Representing the confounder as a latent variable, we propose Intact-VAE, a new variant of variational autoencoder (VAE), motivated by the prognostic score that is sufficient for identifying treatment effects. We theoretically show that, under certain settings, treatment effects are identified by our model, and further, based on the identifiability of our model (i.e., determinacy of representation), our VAE is a consistent estimator with representation balanced for treatment groups. Experiments on (semi-)synthetic datasets show state-of-the-art performance under diverse settings.