E-Books →Bayesian Nonparametrics for Causal Inference and Missing Data

The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials.
Features
* Thorough discussion of both BNP and its interplay with causal inference and missing data
* How to use BNP and g-computation for causal inference and non-ignorable missingness
* How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions
* Detailed case studies illustrating the application of BNP methods to causal inference and missing data
* R code and/or packages to implement BNP in causal inference and missing data problems
The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.