Identification and multiply robust estimation of causal effects via instrumental variables from an auxiliary population
Holder： Wei Li(Renmin University of China)
Time：2026-05-12 14:00-15:00
Location：Room 220 of the New Public Health Building of Peking University Medical School
Abstract:
Estimating causal effects in a target population with unmeasured confounders is challenging, especially when instrumental variables (IVs) are unavailable. However, IVs from auxiliary populations with similar problems can help infer causal effects in the target population. While the homogeneous conditional average treatment effect assumption has been widely used for effect transportability, it has not been explored in IV-based data fusion. We include it as a basic approach, though it may be biased when treatment effect heterogeneity exists. As an alternative approach, we introduce the equi-confounding assumption that the unmeasured confounding bias remains the same after adjusting for observed covariates, while allowing conditional average treatment effects to differ across populations. This allows us to identify the confounding bias in the auxiliary population and remove it from the treatment-outcome association in the target population to recover the causal effect. We develop multiply robust estimators under both approaches and demonstrate them through simulation studies and a real data application.
About the Speaker:
李伟，中国人民大学统计学院副教授、吴玉章青年学者，入选北京市通州区“运河英才计划”领军人才，教育部青年长江学者。研究方向是因果推断、缺失数据及相关领域的应用，在Biometrika, JASA, JRSSB等统计学权威期刊发表文章30余篇。主持国家自然科学基金青年项目和面上项目、北京市自然科学基金面上项目等多项科研课题。个人主页：https://weiliruc.github.io/
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