Description
Book SynopsisBayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest.
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.
Table of Contents
Part I. Overview of Bayesian inference in causal inference and missing data and identifiability. 1. Overview of causal inference. 2. Overview of missing data. 3. Overview of Bayesian Inference for Missing Data and Causal Inference. Part II. Bayesian nonparametrics for causal inference and missing data. 4. Identifiability and Sensitivity Analysis. 5. Bayesian Decision Trees and their Ensembles. Part III. Identification and sensitivity analysis. 6. Dirichlet Process Mixtures and extensions. 7. Gaussian process prior and Dependent Dirichlet processes. 8. Causal Inference on Quantiles using Propensity scores. 9. Causal Inference with a point treatment using an EDPM model. 10. DDP+GP for causal inference using marginal structural models. 11. DPMs for Dropout in Longitudinal Studies. 12. DPMs for Non-Monotone Missingness.
