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.

Donlapark Ponnoprat is a Lecturer in the Department of Statistics at Chiang Mai University, where he teaches courses on machine learning and statistics. His research focuses on developing novel methods for estimation and inference in high-dimensional data. He earned his bachelor's degrees in mathematics and economics from Brown University, and Ph.D. in mathematics from the University of California San Diego.