Propensity score weighting under limited overlap and model misspecification

Seminar Series

Friday, September 25, 2020 - 10:00
Zoom Conference
Roland Matsouaka, PhD

Abstract: Propensity score (PS) weighting methods are often used in non-randomized studies to adjust for confounding and assess treatment effects. The most popular among them, the inverse probability weighting (IPW), assigns weights that are proportional to the inverse of the conditional probability of a specific treatment assignment, given observed covariates. A key requirement for IPW estimation is the positivity assumption, i.e., the PS must be bounded away from 0 and 1. However, in practice, violations of the positivity assumption often manifest by the presence of limited overlap in the PS distributions between treatment groups. When these practical violations occur, a small number of highly influential IPW weights may lead to unstable IPW estimators, with biased estimates and large variances.

In this talk, we compare a number of alternative methods have been proposed to mitigate these issues, including IPW trimming, overlap weights (OW), matching weights (MW), and entropy weights (EW). We conduct extensive simulation studies to compare the performances of IPW and IPW trimming against those of OW, MW, and EW under limited overlap and misspecified propensity score models. We illustrate the methods with a study of the effect of maternal smoking on infant birthweight. 

Roland Matsouaka, PhD
Assistant Professor
Department of Biostatistics and Bioinformatics
Duke University School of Medicine