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.
From the reviews: "I should begin by noting that the first time I read the book's title I thought that it would address the topic of classical epidemiological designs like case-control studies, cohort studies, etc. However, I was wrong. Rosenbaum's book addresses the crucial topic of designing and analyzing empiric non-randomized investigations to prove causal relationships between treatments and outcomes. These types of studies, the observational ones, are prone to present two well-known selection biases: overt biases, i.e., differences in outcomes between treatments may reflect measured pre-treatment differences between groups rather than effects of treatments, and hidden biases, i.e., the same situation but with pre-treatment differences that were not recorded in the study. To overcome overt biases, applied statisticians usually advocate the use of model-based adjustments and to overcome hidden biases they usually recommend designing a randomized experiment. However, Rosenbaum's book addresses these two biases with an alternative approach: propensity score matching for overt biases and sensitivity analysis for hidden biases. Practitioners may think that sensitivity analysis means performing several analyses of the same data set; however, this is not the Rosenbaum's approach. Overall, the book is written in a clear and concise way, merging theoretical and practical aspects. Small examples are provided to develop the understanding of key issues in parallel with real examples of practical size from both the economics and the biomedicine areas. Moreover, although the book is not intended as a statistical software oriented book, the text includes some code in R and SAS. For example, Chapter 13 is devoted to matching in R. 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 naive 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) "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)