Introduction to Modern Randomization-Based Design and Analysis for Causal Inference
暫譯: 現代隨機化設計與因果推斷分析導論
Dasgupta, Tirthankar, Rubin, Donald B.
- 出版商: CRC
- 出版日期: 2026-09-22
- 售價: $3,820
- 貴賓價: 9.5 折 $3,629
- 語言: 英文
- 頁數: 340
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 0367500981
- ISBN-13: 9780367500986
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相關分類:
機率統計學 Probability-and-statistics
尚未上市，無法訂購
商品描述
Design of experiments is, in essence, a disciplined way to learn about cause and effect. Modern experiments can involve a few to millions of units and hundreds or thousands of covariates. These settings demand tools that are flexible, transparent, and faithful to the underlying design in order reach reliable conclusions about which interventions work and which ones do not. This book provides a modern, accessible, and computationally supported introduction to experimental design grounded firmly in randomization and the formulation of ideas and methods in terms of potential outcomes. Instead of prescribing a model for each design, we begin with the treatment assignment mechanism and link it directly to the observed outcomes through the potential outcomes framework. This formulation illuminates how changing the design changes the analysis, and it naturally distinguishes finite-population inference from super-population modeling. The book also incorporates new developments at the interface of causal inference and experimental design, many stemming from the authors' recent collaborative research efforts.
Key Features:
- Strengthens the link between design and analysis, enabling students to see immediately how the structure of an experiment shapes the exact tools used to analyze it.
- Teaches foundational concepts without assuming linear-model assumptions.
- Equips readers with the tools needed to analyze non-standard and complex experiments, whose randomization mechanisms fall outside the scope of traditional textbooks.
- Support students with limited programming experience by providing algorithms and code throughout the book, enabling them to implement randomization-based methods easily and efficiently.
This book is a textbook for one/two semester course on introductory experimental design.
商品描述(中文翻譯)
實驗設計本質上是一種有紀律的學習因果關係的方法。現代實驗可以涉及幾個到數百萬個單位，以及數百或數千個協變量。這些情境需要靈活、透明且忠實於基本設計的工具，以便對哪些干預措施有效、哪些無效得出可靠的結論。本書提供了一個現代、易於理解且具計算支持的實驗設計入門，堅實地基於隨機化及以潛在結果的形式來表述思想和方法。我們不會為每個設計規定一個模型，而是從處理分配機制開始，並通過潛在結果框架將其直接與觀察到的結果聯繫起來。這種表述清楚地顯示了設計變更如何影響分析，並自然區分有限母體推斷與超母體建模。本書還融入了因果推斷與實驗設計交界處的新發展，許多來自作者最近的合作研究努力。
主要特點：
- 加強設計與分析之間的聯繫，使學生能立即看到實驗的結構如何塑造用於分析的具體工具。
- 教授基礎概念，而不假設線性模型的假設。
- 裝備讀者分析非標準和複雜實驗所需的工具，這些實驗的隨機化機制超出了傳統教科書的範疇。
- 通過在全書中提供算法和代碼，支持有限編程經驗的學生，使他們能夠輕鬆高效地實施基於隨機化的方法。
本書是針對一學期或兩學期的入門實驗設計課程的教科書。
作者簡介
Tirthankar Dasgupta is a Professor of Statistics at Rutgers University, New Jersey. Prior to joining Rutgers University, he served as a faculty member at Harvard University. His primary research interests include experimental design and causal inference. He is a fellow of the American Statistical Association. He has published about 50 peer-reviewed research articles and has served on the editorial boards of several leading journals of statistics including the Journal of the American Statistical Association, Journal of the Royal Statistical Society (Series B) and Statistical Science.
Donald B. Rubin is an Emeritus Professor of Statistics at Harvard University, and is currently affiliated with Temple University. He is most well known for the Rubin causal model, a set of methods designed for causal inference with observational data, and for his methods for dealing with missing data. Professor Rubin is a fellow/member/honorary member of the Woodrow Wilson Society, Guggenheim Memorial Foundation, Alexander von Humboldt Foundation, American Statistical Association, Institute of Mathematical Statistics, International Statistical Institute, American Association for the Advancement of Science, American Academy of Arts and Sciences, European Association of Methodology, the British Academy, and the US National Academy of Sciences. He has authored or co-authored about 450 publications (including 10 books), has four joint patents, and is one of the most highly cited authors in the world, with nearly 450,000 citations.
作者簡介(中文翻譯)
Tirthankar Dasgupta 是新澤西州羅格斯大學的統計學教授。在加入羅格斯大學之前，他曾在哈佛大學擔任教職。他的主要研究興趣包括實驗設計和因果推斷。他是美國統計協會的會士。他已發表約50篇經過同行評審的研究文章，並曾擔任多本領先統計期刊的編輯委員會成員，包括《美國統計協會期刊》、《英國皇家統計學會期刊（B系列）》和《統計科學》。
Donald B. Rubin 是哈佛大學的名譽統計學教授，目前與天普大學有關聯。他最著名的貢獻是Rubin因果模型，這是一套針對觀察數據進行因果推斷的方法，以及他處理缺失數據的方法。Rubin教授是伍德羅·威爾遜學會、古根海姆紀念基金會、亞歷山大·馮·洪堡基金會、美國統計協會、數學統計學會、國際統計學會、美國科學促進會、美國藝術與科學學院、歐洲方法學協會、英國學院和美國國家科學院的會士/成員/名譽成員。他已發表或共同發表約450篇出版物（包括10本書），擁有四項共同專利，並且是全球引用次數最高的作者之一，引用次數接近450,000次。
