Estimating average causal effects with incomplete exposure and confounders.

ABSTRACT: Standard methods for estimating average causal effects require complete observations of the exposure and confounders. In observational studies, however, missing data are ubiquitous. Motivated by a study on the effect of prescription opioids on mortality, we propose methods for estimating average causal effects when exposures and potential confounders may be missing. We consider missingness at random and additionally propose several specific missing not at random (MNAR) assumptions. Under our proposed MNAR assumptions, we show that the average causal effects are identified from the observed data and derive corresponding influence functions, which form the basis of our proposed estimators. Our simulations show that standard multiple imputation techniques paired with a complete data estimator

SUBMITTER: Wen L 

PROVIDER: S-EPMC12922761 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature