Conditional Average Treatment Effect Estimation Under Hidden Confounders
Journal:
arXiv
Published Date:
Jun 14, 2025
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.
