Statistical issues in general (EPI: unconfounding in observational studies)
June 17, 2026 In this paper, the authors have described a simple strategy for empirically assessing the plausibility of conditional unconfoundedness (i.e., whether the candidate adjustment set of covariates suffices for confounding adjustment), which does not require any explicit assumptions about the confounding structure, relying instead on assumptions related to temporal ordering between covariates, exposure, and outcome (which can be guaranteed by design) and selection into the study. The proposed method essentially has relied on testing the association between a subset of the covariates included in the adjustment set (those associated with the exposure, given all other covariates) and the outcome conditional on the remaining covariates and the exposure. Confounding has been one of the major limitations of causal inference. In this paper, they used the word “confounding” to mean the existence of any open backdoor path (a term from causal inference regarding confounding. The authors went through simulations and a real word data example to show the benefit of their method to assess the plausibility of conditional unconfoundedness. The authors considered several relevant aspects of an epidemiological study, including temporal relationships between variables and the possibility of selection bias and measurement error. In addition to their