主 题：Principal Stratification with Continuous Post-Treatment Variables: Nonparametric Identification and Semiparametric Estimation

带有连续治疗后变量的主要分层：非参数识别和半参数估计

主讲人：加州大学伯克利分校丁鹏副教授

主持人：统计学院林华珍教授

时间：2024年7月16日（周二）上午11：00-12：00

举办地点：柳林校区弘远楼408会议室

主办单位：统计研究中心和统计学院 科研处

主讲人简介：

Peng Ding is an Associate Professor in the Department of Statistics at UC Berkeley. He obtained his Ph.D. from the Department of Statistics, Harvard University in May 2015, and worked as a postdoctoral researcher in the Department of Epidemiology, Harvard T. H. Chan School of Public Health until December 2015. Previously, he received my B.S. in Mathematics, B.A. in Economics, and M.S. in Statistics from Peking University.

丁鹏，加州大学伯克利分校统计系的副教授。他于2015年5月在哈佛大学统计系获得博士学位，并在2015年12月之前在哈佛大学陈曾熙公共卫生学院流行病学系担任博士后研究员。在此之前，他获得了北京大学的数学学士学位、经济学学士学位和统计学硕士学位。

内容简介：

Post-treatment variables often complicate causal inference. They appear in many scientific problems, including noncompliance, truncation by death, mediation, and surrogate endpoint evaluation. Principal stratification is a strategy to address these challenges by adjusting for the potential values of the post-treatment variables, defined as the principal strata. It allows for characterizing treatment effect heterogeneity across principal strata and unveiling the mechanism of the treatment's impact on the outcome related to post-treatment variables. However, the existing literature has primarily focused on binary post-treatment variables, leaving the case with continuous post-treatment variables largely unexplored. This gap persists due to the complexity of infinitely many principal strata, which present challenges to both the identification and estimation of causal effects. We fill this gap by providing nonparametric identification and semiparametric estimation theory for principal stratification with continuous post-treatment variables. We propose to use working models to approximate the underlying causal effect surfaces and derive the efficient influence functions of the corresponding model parameters. Based on the theory, we construct doubly robust estimators and implement them in an R package.

治疗后变量通常会使因果推断变得复杂。它们出现在许多科学问题中，包括不遵从、死亡截断、中介效应和替代终点评估。主要分层是一种通过调整治疗后变量的潜在值（即主要分层）来解决这些挑战的策略。它允许表征不同主要分层中的治疗效果异质性，并揭示治疗对与治疗后变量相关的结果的影响机制。然而，现有文献主要集中在二元治疗后变量上，对于连续治疗后变量的情况则研究较少。由于无限多的主要分层的复杂性，这一领域在因果效应的识别和估计方面面临挑战。主讲人通过提供连续治疗后变量主要分层的非参数识别和半参数估计理论填补了这一空白。主讲人提出使用工作模型来逼近潜在的因果效应面，并推导出相应模型参数的有效影响函数。基于该理论，主讲人构建双重稳健估计量，并在R软件包中实现这些方法。