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Causal Inference with Bayesian Networks. Build Bayesian Networks and Causal Inference Models with R and Python
Yousri El Fattah, Reza Bagheri
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This practical guide explores Bayesian networks, graphical models, and causal inference for probabilistic reasoning and treatment effect estimation using real-world data. You’ll learn Bayesian networks, conditional independence, structural causal models (SCM), and intervention-based reasoning for causal analysis. The book explains how graphical models support probabilistic inference, decision-making, and knowledge representation across healthcare, economics, epidemiology, finance, and social sciences.
You’ll work with probabilistic inference methods such as variable elimination, tree clustering, and Bayesian network reasoning. For causal inference, the book covers Pearl’s do-calculus, backdoor and front-door criteria, causal effect identification, and treatment effect estimation using observational data. You’ll also explore the potential outcomes framework and machine learning approaches for causal inference, including meta-learners for estimating conditional average treatment effects and heterogeneous treatment effects.
Practical examples and exercises in R and Python help reinforce concepts and build implementation skills for causal modeling workflows. By the end of the book, you’ll be able to design Bayesian network models, perform probabilistic and causal inference, and develop practical causal analysis applications for evidence-based decision-making.
You’ll work with probabilistic inference methods such as variable elimination, tree clustering, and Bayesian network reasoning. For causal inference, the book covers Pearl’s do-calculus, backdoor and front-door criteria, causal effect identification, and treatment effect estimation using observational data. You’ll also explore the potential outcomes framework and machine learning approaches for causal inference, including meta-learners for estimating conditional average treatment effects and heterogeneous treatment effects.
Practical examples and exercises in R and Python help reinforce concepts and build implementation skills for causal modeling workflows. By the end of the book, you’ll be able to design Bayesian network models, perform probabilistic and causal inference, and develop practical causal analysis applications for evidence-based decision-making.
- 1. A Guided Tour of Book Topics
- 2. Probability and Bayes' Theorem
- 3. Bayesian Networks
- 4. Structural Causal Models
- 5. Relational Database Models
- 6. Join Tree Clustering
- 7. Probabilistic Inference with Join Tree Clustering
- 8. Probabilistic Inference with Relational Database Models
- 9. Causal Inference with Structural Causal Models
- 10. Causal Inference with Observational Data
- 11. Causal Inference with Machine Learning
- 12. Causal Inference in Economic Research
- 13. Causal Inference in Epidemiology
- 14. Causal Inference in Social Science Research
- Title:Causal Inference with Bayesian Networks. Build Bayesian Networks and Causal Inference Models with R and Python
- Author:Yousri El Fattah, Reza Bagheri
- Original title:Causal Inference with Bayesian Networks. Build Bayesian Networks and Causal Inference Models with R and Python
- ISBN:9781835089217, 9781835089217
- Date of issue:2026-05-29
- Format:Ebook - EPUB
- Item ID: e_3w84
- Publisher: Packt Publishing
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