报告题目：Inference for mark-specific causal effects

报告人： 曲连强 教授 （华中师范大学）

报告时间：2026年11月22 日（周日）上午8：30-9：30

报告地点：学院会议室106

报告摘要: This paper presents a framework for causal inference in the presence of censored data, where the failure time is marked by a continuous variable referred to as a mark. The mark is observed after treatment and is not meaningful when the failure time is censored. In addition, due to the continuous nature of the marks, observations at each given mark are sparse. These facts make the identification and estimation of causality a challenging task. To address these issues, we define a new mark-specific treatment effect within the potential outcomes framework and characterize its identifiability conditions. We then propose a local smoothing estimator and establish its asymptotic properties. We further develop testing methods to evaluate whether the treatment has an effect on the failure time when controlling the values of the mark at certain points or within a defined interval, and develop a Gaussian approximation method to obtain the critical values. In addition, we develop a varying-coefficient regression adjusted estimator that incorporates covariate information and extend the proposed methods to observational studies. We evaluate our method using simulation studies as well as a real dataset from the Antibody Mediated Prevention trials.

报告人简介：曲连强现为华中师范大学副教授。主要研究方向为生存分析和复杂数据统计推断。现已发表学术论文19篇，包括JASA, Biometrika，JRSSB, JMLR 以及JBES等。