Conditional Average Treatment Effect Estimation Under Hidden Confounders

TL;DR

This paper introduces a novel method for estimating conditional average treatment effects in the presence of hidden confounders by leveraging small RCT datasets without covariate information, enhancing accuracy in practical scenarios.

Contribution

The authors propose a pseudo-confounder generator and alignment technique that effectively combines observational and RCT data without covariate assumptions, addressing hidden confounders.

Findings

Effective in synthetic datasets

Demonstrates improved CATE estimation accuracy

Applicable to privacy-sensitive medical data

Abstract

One of the major challenges in estimating conditional potential outcomes and conditional average treatment effects (CATE) is the presence of hidden confounders. Since testing for hidden confounders cannot be accomplished only with observational data, conditional unconfoundedness is commonly assumed in the literature of CATE estimation. Nevertheless, under this assumption, CATE estimation can be significantly biased due to the effects of unobserved confounders. In this work, we consider the case where in addition to a potentially large observational dataset, a small dataset from a randomized controlled trial (RCT) is available. Notably, we make no assumptions on the existence of any covariate information for the RCT dataset, we only require the outcomes to be observed. We propose a CATE estimation method based on a pseudo-confounder generator and a CATE model that aligns the learned potential outcomes from the observational data with those observed from the RCT. Our method is applicable to many practical scenarios of interest, particularly those where privacy is a concern (e.g., medical applications). Extensive numerical experiments are provided demonstrating the effectiveness of our approach for both synthetic and real-world datasets.