Matched Sampling for Causal Effects (Paperback)

Donald B. Rubin

ISBN 10: 0521674360 / ISBN 13: 9780521674362

Edité par Cambridge University Press, Cambridge, 2006

A propos de cet article

Paperback. 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

N° de réf. du vendeur 9780521674362

Détails bibliographiques

• Titre: Matched Sampling for Causal Effects (Paperback)
• Éditeur: Cambridge University Press, Cambridge
• Date de parution: 2006
• Langue: anglais
• ISBN à 10 chiffres: 0521674360
• ISBN à 13 chiffres: 9780521674362
• Reliure: Paperback
• Édition: Edition originale
• État de l'article: new

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Professor Donald B. Rubin is the John L. Loeb Professor of Statistics in the Department of Statistics at Harvard University. Professor Rubin is a fellow of the American Statistical Association, the Institute for Mathematical Statistics, the International Statistical Institute, the Woodrow Wilson Society, the John Simon Guggenheim Society, the New York Academy of Sciences, the American Association for the Advancement of Sciences, and the American Academy of Arts and Sciences. He is also the recipient of the Samuel S. Wilks Medal of the American Statistical Association, the Parzen Prize for Statistical Innovation, and the Fisher Lectureship. Professor Rubin has lectured extensively throughout the United States, Europe, and Asia. He has over 300 publications (including several books) on a variety of statistical topics and is one of the top ten highly cited writers in mathematics in the world, according to ISI Science Watch.