Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction (Hardcover)

Guido W. Imbens, Donald B. Rubin

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商品描述

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including, matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

商品描述(中文翻譯)

社會科學和生物醫學科學中的大多數問題都是因果關係的:如果他們的環境的一部分被改變,個體或群體會發生什麼變化?在這本開創性的著作中,兩位世界知名專家介紹了研究這些問題的統計方法。本書從潛在結果的概念開始,每個潛在結果對應於如果一個受試者接受特定治療或方案時會實現的結果。在這種方法中,因果效應是這些潛在結果的比較。因果推斷的基本問題是,我們只能觀察到特定受試者的其中一個潛在結果。作者討論了隨機實驗如何讓我們評估因果效應,然後轉向觀察研究。他們列出了因果推斷所需的假設,並描述了主要的分析方法,包括匹配、傾向分數方法和儀器變量。書中包含了許多詳細的應用,特別關注實證研究者的實際方面。