{"product_id":"bayesian-regression-and-causal-inference","title":"Bayesian Regression and Causal Inference","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.","brand":"Gardners","offers":[{"title":"Default Title","offer_id":57510177833333,"sku":"9783032192226","price":99.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0612\/7193\/3106\/files\/9783032192226.jpg?v=1787130510","url":"https:\/\/backstory.london\/products\/bayesian-regression-and-causal-inference","provider":"Backstory","version":"1.0","type":"link"}