Overlap in observational studies with high-dimensional covariates

Estimating causal effects under exogeneity hinges on two key assumptions: unconfoundedness and overlap. Researchers often argue that unconfoundedness is more plausible when more covariates are included in the analysis. Less discussed is the fact that covariate overlap is more difficult to satisfy in this setting. In this paper, we explore the implications of overlap in observational studies with high-dimensional covariates and formalize curse-of-dimensionality argument, suggesting that these assumptions are stronger than investigators likely realize. Our key innovation is to explore how strict overlap restricts global discrepancies between the covariate distributions in the treated and control populations. Exploiting results from information theory, we derive explicit bounds on the average imbalance in covariate means under strict overlap and show that these bounds become more restrictive as the dimension grows large. We discuss how these implications interact with assumptions and procedures commonly deployed in observational causal inference, including sparsity and trimming. (C) 2020 The Authors. Published by Elsevier B.V.

Automated summary

This paper discusses the key assumptions for estimating causal effects under exogeneity, including unconfoundedness and overlap. Researchers often argue that unconfoundedness is more plausible when more covariates are included in the analysis, while less discussed is the difficulty of satisfying covariate overlap. By exploiting results from information theory, the authors derive explicit bounds on the average imbalance in covariate means under strict overlap, showing that these bounds become more restrictive as the dimension grows large.