Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers.
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Editorial Reviews
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"A useful reference for the student of statistics interested in this area."
Angie Wade, University College, London
"... Researchers and practitioners are both likely to find something of interest to them in this book. In particular, researchers in causal inference who don't already have most of these papers - particularly those just starting out in the field - will be well served by this compact assembly of papers."
Biometrics
Angie Wade, University College, London
"... Researchers and practitioners are both likely to find something of interest to them in this book. In particular, researchers in causal inference who don't already have most of these papers - particularly those just starting out in the field - will be well served by this compact assembly of papers."
Biometrics
Book Description
This selection of Donald B. Rubin's contributions to matched sampling includes classic theoretical studies as well as real applications. Each of the seven parts has an introduction by the author offering historical and current context. The book thus provides an accessible introduction to the study of matched sampling for readers across the statistical, social, and medical sciences.
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31 of 31 people found the following review helpful:
4.0 out of 5 stars
causal inference by one of the originators,
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This review is from: Matched Sampling for Causal Effects (Paperback)
An important issue for researchers is the discovery of cause and effect relationships. It is often the case that those not well educated in statistics will think that a simple correlation between two variables is enough to imply causation (perhaps because of temporal order i.e. A comes before B so A causes B). However, determining causation is a much more complicated issue. A common statistical adage is "correlation does not imply causation". Don Rubin is an accomplished author, teacher and one of the leading developers of the statistical theory of causation. the authorities to read on this subject are Don Rubin and Judea Pearl, two of the pioneers who have written texts on this topics. This theory involves new concepts that one does not learn in introductory statistics course. Contrafactuals represent one such concept. Read this book to learn the details.
9 of 9 people found the following review helpful:
5.0 out of 5 stars
Great book!,
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This review is from: Matched Sampling for Causal Effects (Paperback)
This is a very good collection of important articles that Rubin and his students and colleagues have written on causal inference.
Another book written by his student P. Rosenbaum (now a Wharton prof) titled "Observtional Studies" is also very important if one wants to learn the details of propensity scoring or matching.
5.0 out of 5 stars
Reprints, but worth the Rereads,
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This review is from: Matched Sampling for Causal Effects (Paperback)
This is a collection of earlier journal articles by Don Rubin and his collaborators, with some interesting personal narrative to situate each substantive section of the volume. Every chapter is worth reading and the volume is a comprehensive collection of his most important work on the area of subclassification, propensity scores, and matching. There is more recent work by other scholars that complicates some of the theoretical and statistical ideas presented here, but Rubin's (and Cochran's and Rosenbaum's) contributions to causal inference is slowly reshaping applied statistics by directing attention at approaching causal inference. Pearl's Causality: Models, Reasoning and Inference offers a completely different view of causal inference, but Rubin's work is more accessible and more practical for most present applications.
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Even though this book is limited to reprinting publications of mine on matched sampling, it seems useful to provide some background history to the topic. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Rubin Abstract, American Statistical Association, Neal Thomas, International Biometric Society, United States, General Accounting Office, Harvard University, Rubin Table, Analytical Studies, March of Dimes, Multichild Scholarship, Paul Rosenbaum, Peters Belson, Subtotal Total Single Scholarship, Surgeon General's Committee New!
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Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Even though this book is limited to reprinting publications of mine on matched sampling, it seems useful to provide some background history to the topic. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Rubin Abstract, American Statistical Association, Neal Thomas, International Biometric Society, United States, General Accounting Office, Harvard University, Rubin Table, Analytical Studies, March of Dimes, Multichild Scholarship, Paul Rosenbaum, Peters Belson, Subtotal Total Single Scholarship, Surgeon General's Committee New!
Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Citations (learn more)
This book cites 37
books:
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cites this book:
- Observational Studies (Springer Series in Statistics) by Paul R. Rosenbaum on 8 pages
- Planning and Analysis of Observational Studies (Wiley Series in Probability and Statistics) by William G. Cochran on page 27, and Back Matter
- Experimental Designs by William Gemmel Cochran in Back Matter (1), and Back Matter (2)
- Continuous Univariate Distributions, Vol. 1 (Wiley Series in Probability and Statistics) by Norman L. Johnson in Back Matter
- Symmetric Multivariate and Related Distributions (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Kai-Tai Fang in Back Matter
See all 37 books this book cites
- Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman in Back Matter
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