30: Methods for Addressing Unmeasured Confounding in Observational Studies

Music City Center 

A key challenge in estimating the causal effect of a treatment on an outcome in observational studies is unmeasured confounding, which causes bias. Traditional techniques such as propensity score-based matching, stratification, and marginal structural models can control for measured confounding but are inadequate to deal with unmeasured confounding. Several advanced methods have been proposed to tackle unmeasured confounding, such as the instrumental variable (IV) approach, regression discontinuity design (RDD), and difference in difference (DID). These methods exploit assignment mechanisms that determine treatment status but are not related to any unmeasured confounding. In this presentation, we will first explore the issues arising from unmeasured confounding, then provide an overview of commonly used methods for addressing unmeasured confounding, including the assumptions, key concepts, and implementations. Finally, we will review examples of how these advanced methods are applied in clinical and healthcare research.

Unmeasured confounding

causal inference

instrumental variable

regression discontinuity

observational studies 

Section on Statistics in Epidemiology