Synopsis
Beginnings.- Dilemmas and Craftsmanship.- Causal Inference in Randomized Experiments.- Two Simple Models for Observational Studies.- Competing Theories Structure Design.- Opportunities, Devices, and Instruments.- Transparency.- Matching.- A Matched Observational Study.- Basic Tools of Multivariate Matching.- Various Practical Issues in Matching.- Fine Balance.- Matching Without Groups.- Risk-Set Matching.- Matching in R.- Design Sensitivity.- The Power of a Sensitivity Analysis and Its Limit.- Heterogeneity and Causality.- Uncommon but Dramatic Responses to Treatment.- Anticipated Patterns of Response.- Planning Analysis.- After Matching, Before Analysis.- Planning the Analysis.
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Review
From the book reviews:
“The book is both a pragmatic how-to manual and a delightfully written manifesto. ... The book contains four parts. ... People very new to statistics could read this book quite readily, with the caveat that mathematical notation, language, and prose is used far more than in most classic introductory statistics books. This book seems most useful for statisticians and applied statisticians who are looking to learn about a new area that they might not have been exposed to before.” (Luke W. Miratrix, Psychometrika, Vol. 79, 2014)
“This is for those who want to improve whatever their basic design is―for those people, this is a very good book. ... have several quick, but useful, guides: key elements in a design, solutions to common problems (very useful), a symbol glossary, a listing of acronyms, a glossary of statistical terms, suggested readings for a course on the design of observational studies, and an index. ... anyone who is not an expert on the varieties of matching will profit from reading this.” (Richard Goldstein, Technometrics, Vol. 53 (2), May, 2011)
Finally, the book covers all the relevant issues in designing and analyzing treatment effects in observational studies, with the exception of observer bias, i.e., the bias present when the assessment of the outcomes are not valid; see Haro et al. (2006) for further details. Nowadays, it is yet unusual to address hidden biases in observational studies and, therefore, this book is an essential reading for statisticians who want to go a step beyond from the likely naïve sentence: 'We assume no unmeasured confounding in the study’.” (Journal of Biopharmaceutical Statistics , 2011, Issue 1)
“Graduate students and researchers in statistics, biostatistics, econometrics, or academic researchers in statistically oriented fields of psychology and social sciences. ... ‘Design of Observational Studies’ talks about statistics. ... The book will be suitable for a seminar course for graduate students with previous knowledge of the subject area, or practicing statisticians seeking guidance in design of observational research and a language to discuss the issues. ‘Design of Observational Studies’ is an important book.” (Erkki P. Liski, International Statistical Review, Vol. 78 (1), 2010)
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