Bayesian Nonparametrics for Causal Inference and Missing Data
暫譯: 因果推斷與缺失數據的貝葉斯非參數方法

貝葉斯非參數（BNP）方法可以靈活地建模聯合或條件分佈，以及函數關係。這些方法連同因果和/或缺失假設，可以與 g-formula 一起使用，以推斷因果效應。

Dr. Daniels received his undergraduate degree from Brown University in Applied Mathematics and doctoral degree from Harvard University in Biostatistics. He has been on the faculty at Iowa State and University of Texas at Austin.

Currently, Dr. Daniels is Professor, Andrew Banks Family Endowed Chair, and Chair in the Department of Statistics at the University of Florida. He is a past president of ENAR. He is a fellow of the American Statistical Association, past chair of the Statistics in Epidemiology Section of the American Statistical Association (ASA), former chair of the Biometrics Section of the ASA, and former editor of Biometrics.

He has received the Lagakos Distinguished Alumni Award from Harvard Biostatistics and the L. Adrienne Cupples Award from Boston University.

He has published extensively on Bayesian methods for missing data, longitudinal data and causal inference and has been funded by NIH R01 grants as PI and/or MPI since 2001. He also has a strong and productive record of collaborative research, with a focus on behavioral trials in smoking cessation and weight management, muscular dystrophy, and HIV.

Dr. Linero received his PhD in Statistics from the University of Florida. He is currently Assistant Professor in the Department of Statistics and Data Sciences at the University of Texas at Austin. His research is broadly focused on developing flexible Bayesian methods for complex longitudinal data, as well as developing tools for model selection, variable selection, and causal inference within the Bayesian nonparametric framework for high-dimensional problems.

Dr. Roy received his PhD in Biostatistics from the University of Michigan. He is currently Professor of Biostatistics and Chair of the Department of Biostatistics and Epidemiology at Rutgers School of Public Health. He directs the biostatistics core of the New Jersey Alliance for Clinical and Translational Science. He is a fellow of the American Statistical Association (ASA) and recipient of the Causality in Statistics Education Award from the ASA. His methodological research has focused on flexible Bayesian methods for causal inference. As a collaborative statistician, he has worked on studies in many areas of medicine and public health, including chronic kidney disease, hepatotoxicity of medications, and SARS-CoV-2.

丹尼爾斯博士於布朗大學獲得應用數學的學士學位，並在哈佛大學獲得生物統計學的博士學位。他曾在愛荷華州立大學和德克薩斯大學奧斯汀分校任教。

目前，丹尼爾斯博士是佛羅里達大學統計系的教授、安德魯·班克斯家庭捐贈講座教授及系主任。他曾擔任ENAR的會長。他是美國統計協會的會士，曾任美國統計協會（ASA）流行病學統計分會的主席，前ASA生物統計分會的主席，以及《生物統計學》的前編輯。

他曾獲得哈佛生物統計學的拉加科斯傑出校友獎和波士頓大學的L. Adrienne Cupples獎。

他在缺失數據、縱向數據和因果推斷的貝葉斯方法方面發表了大量研究，自2001年以來，他作為主要研究者和/或共同主要研究者獲得了NIH R01資助。他在合作研究方面也有著強大且富有成效的紀錄，專注於戒菸和體重管理的行為試驗、肌肉萎縮症和HIV。

林納羅博士於佛羅里達大學獲得統計學博士學位。他目前是德克薩斯大學奧斯汀分校統計與數據科學系的助理教授。他的研究廣泛集中於為複雜的縱向數據開發靈活的貝葉斯方法，以及在貝葉斯非參數框架下為高維問題開發模型選擇、變數選擇和因果推斷的工具。

羅伊博士於密西根大學獲得生物統計學博士學位。他目前是羅格斯公共衛生學院生物統計學的教授及生物統計學與流行病學系主任。他負責新澤西臨床與轉化科學聯盟的生物統計學核心。他是美國統計協會（ASA）的會士，並獲得ASA的因果推斷教育獎。他的方法論研究專注於因果推斷的靈活貝葉斯方法。作為一名合作統計學家，他參與了許多醫學和公共衛生領域的研究，包括慢性腎病、藥物的肝毒性以及SARS-CoV-2。