Uncertainty in the Design Stage of Observational Studies

• Activity Number: 325 - Bayesian Methods for Policy Research
• Type: Invited
• Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Matthew Cefalu* and Corwin Zigler
• Companies: RAND Corporation and Harvard T.H. Chan School of Public Health
• Keywords: Propensity score; Causal inference

Abstract

Estimating causal effects with propensity scores relies upon the availability of treated and untreated units observed at each value of the estimated propensity score. In settings with strong confounding, limited so-called ``overlap'' in propensity score distributions can undermine the empirical basis for estimating causal effects and yield erratic finite-sample performance of existing estimators. We propose a Bayesian procedure designed to estimate causal effects in settings where there is limited overlap in propensity score distributions. Our method estimates causal effects by marginalizing over the uncertainty in whether each observation is a member of an unknown subset for which treatment assignment can be assumed unconfounded.