Potential Outcomes Estimation Under Hidden Confounders

Ahmed Aloui, Juncheng Dong, Ali Hasan, Vahid Tarokh

Keywords: Confounders, Causal Inference, Treatment Effects

TL;DR: We propose a method to estimate CATE by using observational data and outcomes from a small RCT, addressing hidden confounders without assuming covariate information from the RCT.

Abstract:

One of the major challenges in estimating conditional potential outcomes and the 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, only requiring 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 when privacy is under concern (e.g., medical applications). Extensive numerical experiments are provided demonstrating the effectiveness of our approach for both synthetic and real-world datasets.

Primary Area: causal reasoning

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Submission Number: 8972