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Matched Sampling for Causal Effects

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  • 108 tables
  • Page extent: 502 pages
  • Size: 228 x 152 mm
  • Weight: 0.676 kg

Paperback

 (ISBN-13: 9780521674362 | ISBN-10: 0521674360)




MATCHED SAMPLING FOR CAUSAL EFFECTS

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 for ethical or other reasons. 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 few researchers involved in establishing the statistical foundations of the field, to recent contributions in 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 in statistics, epidemiology, medicine, economics, education, sociology, political science, and anyone doing empirical research to evaluate the causal effects of interventions.

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 more than 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.





MATCHED SAMPLING FOR CAUSAL EFFECTS

DONALD B. RUBIN
Harvard University





CAMBRIDGE UNIVERSITY PRESS
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Information on this title: www.cambridge.org/9780521857628

© Cambridge University Press 2006

This publication is in copyright. Subject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without
the written permission of Cambridge University Press.

First published 2006

Printed in the United States of America

A catalog record for this publication is available from the British Library.

Library of Congress Cataloging in Publication Data

Rubin, Donald B.
Matched sampling for causal effects / Donald B. Rubin.
p. cm.
Includes bibliographical references and index.
ISBN-13: 978-0-521-85762-8 (hardback)
ISBN-10: 0-521-85762-7 (hardback)
ISBN-13: 978-0-521-67436-2 (pbk.)
ISBN-10: 0-521-67436-0 (pbk.)
1. Sampling (Statistics) 2. Statistical matching. I. Title.
HA31.2.R82    2006
001.433 – dc22    2006011564

ISBN-13 978-0-521-85762-8 hardback
ISBN-10 0-521-85762-7 hardback

ISBN-13 978-0-521-67436-2 paperback
ISBN-10 0-521-67436-0 paperback

Cambridge University Press has no responsibility for
the persistence or accuracy of URLs for external or
third-party Internet Web sites referred to in this publication
and does not guarantee that any content on such
Web sites is, or will remain, accurate or appropriate.





Contents

Contributor Acknowledgments page ix
    My Introduction to Matched Sampling 1
PART I.  THE EARLY YEARS AND THE INFLUENCE OF WILLIAM G. COCHRAN 5
1.   William G. Cochran’s Contributions to the Design, Analysis, and Evaluation of Observational Studies 7
    Donald B. Rubin (1984)  
2.   Controlling Bias in Observational Studies: A Review 30
    William G. Cochran and Donald B. Rubin (1973)  
PART II.  UNIVARIATE MATCHING METHODS AND THE DANGERS OF REGRESSION ADJUSTMENT 59
3.   Matching to Remove Bias in Observational Studies 62
    Donald B. Rubin (1973)  
4.   The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies 81
    Donald B. Rubin (1973)  
5.   Assignment to Treatment Group on the Basis of a Covariate 99
    Donald B. Rubin (1977)  
PART III.  BASIC THEORY OF MULTIVARIATE MATCHING 115
6.   Multivariate Matching Methods That Are Equal Percent Bias Reducing, I: Some Examples 117
    Donald B. Rubin (1976)  
7.   Multivariate Matching Methods That Are Equal Percent Bias Reducing, Ⅱ: Maximums on Bias Reduction for Fixed Sample Sizes 129
    Donald B. Rubin (1976)  
8.   Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies 142
    Donald B. Rubin (1979)  
9.   Bias Reduction Using Mahalanobis-Metric Matching 160
    Donald B. Rubin (1980)  
PART IV.  FUNDAMENTALS OF PROPENSITY SCORE MATCHING 167
10.   The Central Role of the Propensity Score in Observational Studies for Causal Effects 170
    Paul R. Rosenbaum and Donald B. Rubin (1983)  
11.   Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome 185
    Paul R. Rosenbaum and Donald B. Rubin (1983)  
12.   Reducing Bias in Observational Studies Using Subclassification on the Propensity Score 193
    Paul R. Rosenbaum and Donald B. Rubin (1984)  
13.   Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score 207
    Paul R. Rosenbaum and Donald B. Rubin (1985)  
14.   The Bias Due to Incomplete Matching 217
    Paul R. Rosenbaum and Donald B. Rubin (1985)  
PART V.  AFFINELY INVARIANT MATCHING METHODS WITH ELLIPSOIDALLY SYMMETRIC DISTRIBUTIONS, THEORY AND METHODOLOGY 233
15.   Affinely Invariant Matching Methods with Ellipsoidal Distributions 235
    Donald B. Rubin and Neal Thomas (1992)  
16.   Characterizing the Effect of Matching Using Linear Propensity Score Methods with Normal Distributions 249
    Donald B. Rubin and Neal Thomas (1992)  
17.   Matching Using Estimated Propensity Scores: Relating Theory to Practice 263
    Donald B. Rubin and Neal Thomas (1996)  
18.   Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates 282
    Donald B. Rubin and Neal Thomas (2000)  
PART VI.  SOME APPLIED CONTRIBUTIONS 305
19.   Causal Inference in Retrospective Studies 308
    Paul W. Holland and Donald B. Rubin (1988)  
20.   The Design of the New York School Choice Scholarships Program Evaluation 328
    Jennifer L. Hill, Donald B. Rubin, and Neal Thomas (1999)  
21.   Estimating and Using Propensity Scores with Partially Missing Data 347
    Ralph B. D’Agostino, Jr., and Donald B. Rubin (2000)  
22.   Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation 365
    Donald B. Rubin (2001)  
PART VII.  SOME FOCUSED APPLICATIONS 383
23.   Criminality in XYY and XXY Men 385
    Herman A. Witkin, Sarnoff A. Mednick, Fini Schulsinger, Eskild Bakkestrøm, Karl O. Christiansen, Donald R. Goodenough, Kurt Hirschhorn, Claes Lundsteen, David R. Owen, John Philip, Donald B. Rubin, and Martha Stocking (1976)  
24.   Practical Implications of Modes of Statistical Inference for Causal Effects and the Critical Role of the Assignment Mechanism 402
    Donald B. Rubin (1991)  
25.   In Utero Exposure to Phenobarbital and Intelligence Deficits in Adult Men 426
    June Machover Reinisch, Stephanie A. Sanders, Erik Lykke Mortensen, and Donald B. Rubin (1995)  
26.   Estimating Causal Effects from Large Data Sets Using Propensity Scores 443
    Donald B. Rubin (1997)  
27.   On Estimating the Causal Effects of DNR Orders 455
    Martin W. McIntosh and Donald B. Rubin (1999)  
    Conclusion: Advice to the Investigator 460
References 463
Author Index 483
Subject Index 487




Contributor Acknowledgments

The author would like to thank his co-authors for their contributions to the publications and, where appropriate, their permission to reprint the articles in this book. Current affiliations are listed when known.

William G. Cochran

Paul R. Rosenbaum, The Wharton School, The University of Pennsylvania

Neal Thomas, Pfizer, Statistical Research and Consulting Center

Jennifer L. Hill, Columbia University

Ralph B. D’Agostino, Jr., Wake Forest University School of Medicine

June Machover Reinisch, The Kinsey Institute for Research in Sex, Gender, and Reproduction; Institute of Preventive Medicine, Danish Epidemiological Science Center, Copenhagen University; Vice President of Scientific Affairs, The Museum of Sex

Stephanie A. Sanders, The Kinsey Institute for Research in Sex, Gender, and Reproduction, Indiana University

Martin W. McIntosh, Fred Hutchinson Cancer Research Center, University of Washington

Paul W. Holland, Educational Testing Service

Sarnoff A. Mednick, University of Southern California

Herman A. Witkin
Fini Schulsinger
Eskild Bakkestrøm
Karl O. Christiansen
Donald R. Goodenough
Kurt Hirschhorn
Claes Lundsteen
David R. Owen
John Philip
Martha Stocking
Erik Lykke Mortensen





MATCHED SAMPLING FOR CAUSAL EFFECTS


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