Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Author: Imbens, Guido W.
Brand: Cambridge University Press
- Cambridge University Press
Number Of Pages: 644
Release Date: 06-04-2015
Details: Product Description Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including, matching, propensity score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher. Review "This book offers a definitive treatment of causality using the potential outcomes approach. Both theoreticians and applied researchers will find this an indispensable volume for guidance and reference." Hal Varian, Chief Economist, Google, and Emeritus Professor, University of California, Berkeley "By putting the potential outcome framework at the center of our understanding of causality, Imbens and Rubin have ushered in a fundamental transformation of empirical work in economics. This book, at once transparent and deep, will be both a fantastic introduction to fundamental principles and a practical resource for students and practitioners. It will be required readings for any class I teach." Esther Duflo, Massachusetts Institute of Technology "Causal Inference sets a high new standard for discussions of the theoretical and practical issues in the design of studies for assessing the effects of causes - from an array of methods for using covariates in real studies to dealing with many subtle aspects of non-compliance with assigned treatments. The book includes many examples using real data that arose from the authors’ extensive research portfolios. These examples help to clarify and explain many important concepts and practical issues. It is a book that both methodologists and practitioners from many fields will find both illuminating and suggestive of further research. It is a professional tour de force, and a welcomed addition to the growing (and often confusing) literature on causation in artificial intelligence, philosophy, mathematics and statistics." Paul W. Holland, Emeritus, Educational Testing Service "A comprehensive and remarkably clear overview of randomized experiments and observational designs with as-good-as-random assignment that is sure to become the standard reference in the field." David Card, Class of 1950 Professor of Economics, University of California, Berkeley "This book will be the "Bible" for anyone interested in the statistical approach to causal inference associated with Donald Rubin and his colleagues, including Guido Imbens. Together, they have systematized the early insights of Fisher and Neyman and have then vastly developed and transformed them. In the process they have created a theory of practical experimentation whose internal consistency is mind-boggling, as is its sensitivity to assumptions and its elaboration of the key 'potential outcomes' framework. The authors’ exposition of random assignment experiments has breadth and clarity of coverage, as do their chapters on observational studies that can be readily conceptualized within an experimental framework. Never have experimental principles been better warranted intellectually or better translated into statistical practice. The book is a "must read" for anyone claiming methodological competence in all sciences that rely on experimentation." Thomas D. Cook, Joan
Package Dimensions: 10.3 x 7.3 x 1.5 inches