Comparing causal inference methods for point exposures with missing confounders: a simulation study.

ABSTRACT: Causal inference methods based on electronic health record (EHR) databases must simultaneously handle confounding and missing data. In practice, when faced with partially missing confounders, analysts may proceed by first imputing missing data and subsequently using outcome regression or inverse-probability weighting (IPW) to address confounding. However, little is known about the theoretical performance of such reasonable, but ad hoc methods. Though vast literature exists on each of these two challenges separately, relatively few works attempt to address missing data and confounding in a formal manner simultaneously. In a recent paper Levis et al. (Can J Stat e11832, 2024) outlined a robust framework for tackling these problems together under certain identifying conditions, and introduced

SUBMITTER: Benz L 

PROVIDER: S-EPMC12482880 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature