Abstract

When the study objective includes estimation of causal parameters, applying inclusion/exclusion criteria or the presence of missing data can bias the resulting estimator. In an observational study, a sufficient adjustment set for estimating a causal effect is a set such that confounding is adjusted for. Corresponding properties of covariate sets targeting selection bias have been shown in related work, within a structural casual framework with graphs. In this paper, this problem is revisited within the potential outcome framework. Here, we present sufficient conditions for identifying average causal effects in randomized and observational studies and propose data driven procedures for covariate adjustment safeguarding against selection bias. Joint work with Stina Zetterström.