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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© Cambridge University Press 2006
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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
001.4′33 – 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
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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


