Causal Inference with Bayesian Networks: Build Bayesian Networks and Causal Inference Models with R and Python

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

- A Guided Tour of Book Topics

- Probability and Bayes' Theorem

- Bayesian Networks

- Structural Causal Models

- Relational Database Models

- Join Tree Clustering

- Probabilistic Inference with Join Tree Clustering

- Probabilistic Inference with Relational Database Models

- Causal Inference with Structural Causal Models

- Causal Inference with Observational Data

- Causal Inference with Machine Learning

- Causal Inference in Economic Research

- Causal Inference in Epidemiology

- Causal Inference in Social Science Research