Propensity scores used as overlap weights provide exact covariate balance

Overlap weighting, using weights defined as the probability of receiving the opposite treatment, is a relatively new, alternative propensity score-based weighting technique used to adjust for confounding when estimating causal treatment effects. It has preferable properties compared to inverse probability of treatment weighting, such as exact covariate balance, safeguards against extreme weights and emphasis on medical equipoise, where treatment decisions are most uncertain. In this article, we introduce the overlap weighting methodology, compare it to inverse probability of treatment weighting and provide some strategies for assessing weighting impact, through an applied example of hospital mortality. When the propensity score distributions have large separation, inverse probability of treatment weighting has been shown to produce biased and less efficient estimates of the treatment effect, making overlap weighting a preferred method in such cases.