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- "This book is what it is meant to be--a 'showcase' of different aspects of highly interesting areas of statistics. But even for those not engaged in Bayesian or causal modeling so far, the book is helpful in providing a first insight into the ideas of causal inference, missing data modeling, computation, and Bayesian inference. Those already familiar with the topic get a very appealing book showing them how widespread the applications of the subject are and providing an overview of state-of-the-art research in Bayesian statistics." -- review by Susanne Rassler in Biometrics

- "Within this unique collection, the reader will find many advanced statistical techniques made accessible for the first time. Since many techniques are illustrated within the context of the social sciences and health care, those working in these areas will find it particularly useful. . . . This volume provides an excellent resource for advanced students and researchers alike who are interested in both the background and leading-edge work in the field." -- review by Dawn Holmes in Statistical Methods in Medical Research

- "Methods that are used include propensity measures, instrumental variables and Bayesian inference. These make it an attractive reference book for those who are interested in pursuing data analyses through some of the more up-to-date methods available in an easily accessible format, via the use of real world data selected from a variety of fields ranging from medicine to census surveys. . . . Its concise reference section and the ease with which each author describes the data set and the problem, and then gently guides the reader into the analysis, are its strongest points. This is really appealing and will be useful to applied statisticians and practitioners, as well as to academics who teach courses related to multiple imputation at an advanced undergraduate and post-graduate level." -- review by Isaac Dialsingh in the Journal of the Royal Statistical Society

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