Matching methods for truncation by death problems
针对随机试验中因参与者死亡导致结果缺失的截断死亡问题，提出基于匹配的方法来识别和估计幸存者平均因果效应，并讨论距离度量选择、有放回匹配及敏感性分析等实际问题。
Abstract Even in a carefully designed randomised trial, outcomes for some study participants can be missing, or more precisely, ill defined, because participants had died prior to outcome collection. This problem, known as truncation by death, means that the treated and untreated are no longer balanced with respect to covariates determining survival. Therefore, researchers often utilise principal stratification and focus on the Survivor Average Causal Effect (SACE). We present matching-based methods for SACE identification and estimation. We provide identification results motivating the use of matching and discuss practical issues, including the choice of distance measures, matching with replacement, and post-matching estimators. Because the assumptions needed for SACE identification can be too strong, we also present sensitivity analysis techniques and illustrate their use in real data analysis.