报告题目： Multiply robust estimation of causal  effects using linked data

报告人：罗姗姗 讲师 北京工商大学

报告时间：2023年11月1日（周三）15:00—16:00

报告地点：腾讯会议（会议 ID 345-455-326）

报告摘要: 

   Unmeasured confounding presents a common challenge in observational studies, potentially making standard causal parameters unidentifiable without additional assumptions. Given the increasing availability of diverse data sources, exploiting data linkage offers a potential solution to mitigate unmeasured confounding within a primary study of interest. However, this approach often introduces selection bias, as data linkage is feasible only for a subset of the study population. To address this concern, we explore three nonparametric identification strategies under the assumption that a unit's inclusion in the linked cohort is determined solely by the observed confounders, while acknowledging that the ignorability assumption may depend on some partially unobserved covariates. The existence of multiple identification strategies motivates the development of estimators that effectively capture distinct components of the observed data distribution. Appropriately combining these estimators yields triply robust estimators for the average treatment effect. These estimators remain consistent if at least one of the three distinct parts of the observed data law is correct. Moreover, they are locally efficient if all the models are correctly specified. We evaluate the proposed estimators using simulation studies and real data analysis.

报告人简介: 

  罗姗姗，现为bat365官网登录入口讲师。罗姗姗于2022年获得北京大学统计学博士学位，同年加入bat365官网登录入口。研究兴趣包括因果推断、缺失数据及其在生物医学和社会科学方面的应用。担任中国现场统计学会因果推断分会理事等。