光華講壇——社會名流與企業家論壇第6612期

主 題：Principal Stratification with Continuous Post-Treatment Variables:Nonparametric Identification and Semiparametric Estimation帶有連續治療后變量的主要分層：非參數識別和半參數估計

主講人：加州大學伯克利分校 丁鵬副教授

主持人：統計學院林華珍教授

時間：7月16日 16:00-17:00

舉辦地點：柳林校區弘遠樓 408 會議室

主辦單位：統計研究中心和統計學院 科研處

主講人簡介：

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.

丁鵬，加州大學伯克利分校統計系的副教授。他于2015年5月在哈佛大學統計系獲得博士學位，并在2015年12月之前在哈佛大學陳曾熙公共衛生學院流行病學系擔任博士后研究員。在此之前，他獲得了北京大學的數學學士學位、經濟學學士學位和統計學碩士學位。

內容簡介：

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.

治療后變量通常會使因果推斷變得復雜。它們出現在許多科學問題中，包括不遵從、死亡截斷、中介效應和替代終點評估。主要分層是一種通過調整治療后變量的潛在值（即主要分層）來解決這些挑戰的策略。它允許表征不同主要分層中的治療效果異質性，并揭示治療對與治療后變量相關的結果的影響機制。然而，現有文獻主要集中在二元治療后變量上，對于連續治療后變量的情況則研究較少。由于無限多的主要分層的復雜性，這一領域在因果效應的識別和估計方面面臨挑戰。主講人通過提供連續治療后變量主要分層的非參數識別和半參數估計理論填補了這一空白。主講人提出使用工作模型來逼近潛在的因果效應面，并推導出相應模型參數的有效影響函數。基于該理論，主講人構建雙重穩健估計量，并在 R 軟件包中實現這些方法。