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Bayesian Regression and Causal Inference: With Examples in R
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(Buch) |
Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!
| Inhalt: |
| 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:
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Part I: Linear Regression
- Learn the basics of Bayesian linear regression, model diagnostics, and uncertainty quantification through a probabilistic lens.
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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.
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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.
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Vorankündigung
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