Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

This book brings together a collection of articles onstatistical methods relating to missing data analysis, includingmultiple imputation, propensity scores, instrumental variables, andBayesian inference. Covering new research topicsand real-world examples which do not feature in manystandard texts. The book is dedicated to Professor Don Rubin(Harvard). Don Rubin has made fundamental contributions tothe study of missing data.Key features of the book include:* Comprehensive coverage of an imporant area for both researchand applications.* Adopts a pragmatic approach to describing a wide range ofintermediate and advanced statistical techniques.* Covers key topics such as multiple imputation, propensityscores, instrumental variables and Bayesian inference.* Includes a number of applications from the social and healthsciences.* Edited and authored by highly respected researchers in thearea.