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.