The growing availability of large health 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 (ATO), 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 statistical, epidemiologic, 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 populations at clinical equipoise 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.

翻译：大型健康数据库的日益普及推动了观察性研究在比较效果研究中的广泛应用。与随机试验不同，观察性研究必须针对治疗组间患者特征的系统性差异进行调整。倾向评分方法（包括匹配、加权、分层和回归调整）通过构建在测量协变量方面可比较的组别来解决这一问题。在这些方法中，重叠加权法（OW）已成为一种原理清晰且高效的方法，其强调处于经验性均衡状态的个体，即那些可能接受任一治疗的个体。通过分配与接受相反治疗概率成比例的权重，OW以重叠人群中的平均处理效应（ATO）为目标，在逻辑倾向评分模型下实现精确的协变量均值平衡，并最小化渐近方差。在过去十年中，OW方法已被统计学、流行病学和临床研究界公认为一种有价值的混杂调整工具，并越来越多地应用于临床和健康研究中。鉴于利用观察性数据模拟随机试验的兴趣日益增长，以及OW在实现协变量平衡（随机研究的基本属性）的同时能够优先考虑处于临床均衡状态人群的能力，本文简要概述了OW方法的最新发展，并就其何时成为因果推断的合适选择提供了实用指导。