Violations of the positivity assumption can render conventional causal estimands unidentifiable, including the average treatment effect (ATE), the average treatment effect on the treated (ATT), and the average treatment effect on the controls (ATC). Shifting the inferential focus to their alternative counterparts -- the weighted ATE (WATE), the weighted ATT (WATT), and the weighted ATC (WATC) -- offers valuable insights into treatment effects while preserving internal validity. In this tutorial, we provide a comprehensive review of recent advances in propensity score (PS) weighting methods, along with practical guidance on how to select a primary target estimand (while other estimands serve as supplementary analyses), implement the corresponding PS-weighted estimators, and conduct post-weighting diagnostic assessments. The tutorial is accompanied by a user-friendly R package, ChiPS. We demonstrate the pertinence of various estimators through extensive simulation studies. We illustrate the flow of the tutorial on two real-world case studies: (i) Effect of smoking on blood lead level using data from the 2007-2008 National Health and Nutrition Examination Survey (NHANES); and (ii) Impact of history of sex work on HIV status among transgender women in South Africa.

翻译：正性假设的违反可能导致传统因果估计量不可识别，包括平均处理效应（ATE）、处理组平均处理效应（ATT）与控制组平均处理效应（ATC）。将推断焦点转向其替代指标——加权平均处理效应（WATE）、加权处理组平均处理效应（WATT）与加权控制组平均处理效应（WATC）——能够在保持内部效度的同时，为处理效应提供有价值的洞见。本教程系统综述了倾向得分（PS）加权方法的最新进展，并提供实践指导，涵盖如何选择主要目标估计量（其他估计量作为补充分析）、实施相应的PS加权估计器，以及进行加权后诊断评估。教程配套提供用户友好的R软件包ChiPS。通过大量模拟研究，我们验证了各类估计器的适用性。教程以两个真实案例研究展示分析流程：（i）基于2007-2008年美国国家健康与营养调查（NHANES）数据，分析吸烟对血铅水平的影响；（ii）评估南非跨性别女性中性工作史对HIV感染状况的影响。