Bayesian Regression and Causal Inference

This textbook provides a practical guide to the Bayesian framework for data modeling and causal inference, focusing on model interpretation, diagnostics, and uncertainty quantification. Central to the book is a "learning-by-doing" approach, using concrete examples in R with real-world datasets spanning diverse fields, including education, psychology, medicine, behavioral science, and environmental science.

The book is structured into three parts:

- Part I: Linear Regression - Learn the basics of Bayesian linear regression, model diagnostics, and uncertainty quantification through a probabilistic lens.

- Part II: Generalized Linear Models - Extend your modeling toolkit to handle binary and count data, zero-inflated models, and clustered data structures common in longitudinal studies.

- Part III: Causal Inference - Learn to identify treatment effects from non-experimental data. This section explores classical techniques--including inverse probability weighting, doubly robust estimation, instrumental variables, and difference-in-differences--alongside advanced techniques like synthetic control, doubly robust DiD, and synthetic DiD.