{ "item_title" : "Bayesian Regression and Causal Inference", "item_author" : [" Donlapark Ponnoprat "], "item_description" : "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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/03/219/222/3032192226_b.jpg", "price_data" : { "retail_price" : "119.99", "online_price" : "119.99", "our_price" : "119.99", "club_price" : "119.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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