Bayesian Regression and Causal Inference: With Examples in R

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, inst