一、报告题目

Continuous-time causal models with irregularly spaced longitudinal observations

二、主讲人

杨淑

三、报告时间

2021年10月27日 20：00 – 21：00

四、报告地点

腾讯会议ID: 452 431 809

五、摘要

Structural Nested Models (SNMs) are useful for causal inference of treatment effects in longitudinal observational studies. Most existing works assume that the data are collected at pre-fixed time points for all subjects, which, however, may be restrictive in practice. To deal with irregularly spaced observations, we assume a class of continuous-time SNMs and a martingale condition of no unmeasured confounding (NUC) to identify the causal parameters. We develop the semiparametric efficiency theory and locally efficient estimators for continuous-time SNMs. The new framework allows us to conduct causal analysis respecting the underlying continuous-time nature of data processes. The simulation study shows that the proposed estimator outperforms existing approaches. We estimate the effect of time to initiate highly active antiretroviral therapy on the CD4 count at year 2 from the observational Acute Infection and Early Disease Research Program database.

六、主讲人简介

杨淑，北卡罗来纳州立大学统计系副教授。2009年本科毕业于北京师范大学数学科学学院，2014年博士毕业于爱荷华州立大学。2014-2016年，在哈佛大学从事博士后研究。2016年起任教于北卡罗纳州立大学并先后担任Eli Lilly and Company和Merck & Co的顾问。

杨淑老师主要从事统计学相关的研究，与多位顶尖统计学家和经济学家，例如2021年Nobel奖得主Imbens教授等，有长期合作。主持多项美国环境健康科研中心的基金，获得过Goodnight Early Career Innovators Award, . Ralph E. Powe Junior Faculty Enhancement Award, Distinguished Paper Competition Award等诸多奖励，在Journal of the Royal Statistical Society: Series B, Journal of American Statistical Association, Biometrika等统计学权威期刊上发表论文50余篇。

七、主办单位

非线性期望前沿科学中心

数学与交叉科学研究中心