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Causal Inference with Bayesian Networks: Build Bayesian Networks and Causal Inference Models with R and Python - Couverture souple

Yousri El Fattah; Reza Bagheri

 
9781835084984: Causal Inference with Bayesian Networks: Build Bayesian Networks and Causal Inference Models with R and Python
Couverture souple
ISBN 10 : 1835084982 ISBN 13 : 9781835084984
Editeur : Packt Publishing, 2026

Synopsis

Learn Bayesian networks, graphical models, and causal inference for probabilistic reasoning, treatment effect estimation, and decision-making using observational data with hands-on examples in R and Python.

Key Features

  • Apply Bayesian networks for probabilistic and causal inference.
  • Estimate causal effects from observational data using machine learning.
  • Build practical causal inference workflows in R and Python.

Book Description

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.

What you will learn

  • Build Bayesian networks for knowledge representation
  • Interpret conditional independence in graphical models
  • Apply causal reasoning with structural causal models
  • Perform probabilistic inference with Bayesian networks
  • Identify and estimate causal treatment effects
  • Use machine learning methods for causal inference
  • Implement probabilistic and causal models in R and Python

Who this book is for

This book will serve as a valuable resource for a wide range of professionals including data scientists, software engineers, policy analysts, decision-makers, information technology professionals involved in developing expert systems or knowledge-based applications that deal with uncertainty, as well as researchers across diverse disciplines seeking insights into causal analysis and estimating treatment effects in randomized studies. The book will enable readers to leverage libraries in R and Python and build software prototypes for their own applications.

Table of Contents

  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

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l'auteur

Yousri El Fattah is the CEO of Causal Computing and has taught courses on artificial intelligence and on control systems at multiple universities, contributed many research and development projects on causal modeling for companies in aerospace and industrial automation, and was a senior scientist in information technology at Rockwell and at Teledyne Technologies. El Fattah is a published author of a book on Learning Systems as well as numerous technical articles in encyclopedia, conference proceedings, and journals including Machine Learning, Artificial Intelligence, IEEE and ASME Transactions. He has a Ph.D.in information and computer sciences as well as a Ph.D. in aeronautical engineering.

Data scientist with over 10 years of experience in statistical and predictive analysis, machine learning and mathematical modeling. Passionate about solving critical problems using cutting-edge machine learning tools.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Packt Publishing
Date d'édition
2026
Langue
anglais
ISBN 10 
1835084982
ISBN 13 
9781835084984
Reliure
Broché
Numéro d'édition
1
Nombre de pages
686
Coordonnées du fabricant
non disponible
Personne responsable
GPSR Kontakt
gpsr@libri.de

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Bad Hersfeld
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