Overview
An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.
Full Product Details
Author: José R. Zubizarreta ,
Elizabeth A. Stuart ,
Dylan S. Small ,
Paul R. Rosenbaum
Publisher: Taylor & Francis Ltd
Imprint: Chapman & Hall/CRC
Weight: 1.160kg
ISBN: 9780367609528
ISBN 10: 0367609525
Pages: 634
Publication Date: 11 April 2023
Audience:
College/higher education
,
Professional and scholarly
,
Tertiary & Higher Education
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: In Print
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Reviews
""Edited and written by many prominent researchers in the field, the book covers both classical and modern topics. Each chapter is self-contained, making it a great reference book. The book is organized in a way that related topics are clustered together, enabling readers to easily navigate and read chapter by chapter. Overall, I enjoyed reading this book very much. [...] The book contains numerous real-data examples that aid readers in understanding the concepts and methods. Additionally, many chapters discuss the computational implementation of the corresponding methods. I am confident that researchers and practitioners will find this book to be an excellent resource for adjustment methods."" -Raymond K.W. Wong in Journal of the American Statistical Association, December 2023 ""The book benefits from a comprehensive collection of recent causal inference methods, offering a wide range of perspectives on weighting and matching techniques. While all the methods share the common goal of unbiased causal effect estimation in observational studies, each chapter clearly demonstrates its focus (eg, balancing covariates or using survival outcomes). In particular, each chapter includes data application examples at the end or incorporates application studies throughout. [...] I am grateful that this book contributes to expanding the accessibility of modern causal inference tools, bringing them together in a cohesive manner for researchers and educators who wish to learn, teach, and apply these methods to obtain unbiased causal evidence from —potentially messy and unkind—observational studies."" -Youjin Lee in Biometrics, September 2024
"""Edited and written by many prominent researchers in the field, the book covers both classical and modern topics. Each chapter is self-contained, making it a great reference book. The book is organized in a way that related topics are clustered together, enabling readers to easily navigate and read chapter by chapter. Overall, I enjoyed reading this book very much. [...] The book contains numerous real-data examples that aid readers in understanding the concepts and methods. Additionally, many chapters discuss the computational implementation of the corresponding methods. I am confident that researchers and practitioners will find this book to be an excellent resource for adjustment methods."" -Raymond K.W. Wong in Journal of the American Statistical Association, December 2023"
Author Information
José Zubizarreta, PhD, is an associate professor in the Department of Health Care Policy at Harvard Medical School and in the Department Biostatistics at Harvard University. He is a Fellow of the American Statistical Association, and is a recipient of the Kenneth Rothman Award, the William Cochran Award, and the Tom Ten Have Memorial Award. Elizabeth A. Stuart, Ph.D. is Bloomberg Professor of American Health in the Department of Mental Health, the Department of Biostatistics and the Department of Health Policy and Management at Johns Hopkins Bloomberg School of Public Health. She is a Fellow of the American Statistical Association, and she received the mid-career award from the Health Policy Statistics Section of the ASA, the Gertrude Cox Award for applied statistics, Harvard University’s Myrto Lefkopoulou Award for excellence in Biostatistics, and the Society for Epidemiologic Research Marshall Joffe Epidemiologic Methods award. Dylan Small, PhD is the Universal Furniture Professor in the Department of Statistics and Data Science of the Wharton School of the University of Pennsylvania. He is a Fellow of the American Statistical Association and an Institute of Mathematical Statistics Medallion Lecturer. Paul R. Rosenbaum is emeritus professor of Statistics and Data Science at the Wharton School of the University of Pennsylvania. From the Committee of Presidents of Statistical Societies, he received the R. A. Fisher Award and the George W. Snedecor Award. He is the author of several books, including Design of Observational Studies and Replication and Evidence Factors in Observational Studies.
