Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

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Statistical techniques that take account of missing data in a clinical trial, census, or other experiments, observational studies, and surveys are of increasing importance. The use of increasingly powerful computers and algorithms has made it possible to study statistical problems from a Bayesian perspective. These topics are highly active research areas and have important applications across a wide range of disciplines. This book is a collection of articles from leading researchers on statistical methods relating to missing data analysis, causal inference, and statistical modeling, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. The book is dedicated to Professor Donald Rubin, on the occasion of his 60th birthday, in recognition of his many and wide-ranging contributions to statistics, particularly to the topic of statistical analysis with missing data. * Provides an authoritative overview of several important statistical topics for both research and applications. * Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. * Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. * Includes a range of applications from the social, health, biological, and physical sciences. * Features overview chapters for each part of the book. * Edited and authored by highly respected researchers in the area. Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives presents an overview with examples of these key topics suitable for researchers in all areas of statistics. It adopts a practical approach suitable for applied statisticians working in social and political sciences, biological and medical sciences, and physical sciences, as well as graduate students of statistics and biostatistics.