What is overlap weighting, how has it evolved, and when to use it for causal inference?

The growing availability of large healthcare databases has expanded the use of observational studies for comparative effectiveness research. Unlike randomized trials, observational studies must adjust for systematic differences in patient characteristics between treatment groups. Propensity score methods, including matching, weighting, stratification, and regression adjustment, address this issue by creating groups that are comparable with respect to measured covariates. Among these approaches, overlap weighting (OW) has emerged as a principled and efficient method that emphasizes individuals at empirical equipoise, those who could plausibly receive either treatment. By assigning weights proportional to the probability of receiving the opposite treatment, OW targets the average treatment effect in the overlap population, achieves exact mean covariate balance under logistic propensity score models, and minimizes asymptotic variance. Over the last decade, the OW method has been recognized as a valuable confounding adjustment tool across the statistics, epidemiology, and clinical research communities and is increasingly applied in clinical and health studies. Given the growing interest in using observational data to emulate randomized trials and the capacity of OW to prioritize the region of empirical equipoise in the observed data, which under some conditions approximates, but is not equivalent to, the population that a pragmatic randomized trial would enroll while achieving covariate balance (fundamental attributes of randomized studies), this article provides a concise overview of recent methodological developments in OW and practical guidance on when it represents a suitable choice for causal inference.