Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation

arXiv:2311.11321v2 Announce Type: replace-cross
Abstract: State-of-the-art methods for conditional average treatment effect (CATE) estimation make widespread use of representation learning. Here, the idea is to reduce the variance of the low-sample CATE estimation by a (potentially constrained) low-dimensional representation. However, low-dimensional representations can lose information about the observed confounders and thus lead to bias, because of which the validity of representation learning for CATE estimation is typically violated. In this paper, we propose a new, representation-agnostic refutation framework for estimating …

abstract art arxiv bias confounding cs.ai cs.lg however information low reduce representation representation learning sample state stat.ml treatment type variance