This is a practical guide that explores the theory and application of Bayesian networks (BN) for probabilistic and causal inference. The book provides step-by-step explanations of graphical models of BN and their structural properties; the causal interpretations of BN and the notion of conditioning by intervention; and the mathematical model of structural equations and the representation in structured causal models (SCM).

For probabilistic inference in Bayesian networks, you will learn methods of variable elimination and tree clustering. For causal inference you will learn the computational framework of Pearl's do-calculus for the identification and estimation of causal effects with causal models. In the context of causal inference with observational data, you will be introduced to the potential outcomes framework and explore various classes of meta-learning algorithms that are used to estimate the conditional average treatment effect in causal inference.

The book includes practical exercises using R and Python for you to engage in and solidify your understanding of different approaches to probabilistic and causal inference. By the end of this book, you will be able to build and deploy your own causal inference application. You will learn from causal inference sample use cases for diagnosis, epidemiology, social sciences, economics, and finance.

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Spis treści

Causal Inference with Bayesian Networks. Exploring the Practical Applications and Demonstrations of Causal Inference using R and Python eBook -- spis treści

• 1. Introduction
• 2. Basics of Probability
• 3. Bayesian Networks
• 4. Structured Causal Models
• 5. Relational Database Models
• 6. Probabilistic Inference in Bayesian Networks
• 7. Probabilistic Inference in Relational Database Models
• 8. Causal Inference with Structured Causal Models
• 9. Causal Inference with Observational Data
• 10. Causal Inference using Machine Learning
• 11. Causal Modeling in Factory Automation Diagnostics
• 12. Causal Inference in Economic Research
• 13. Causal Inference in Epidemiology
• 14. Causal Inference in Finance
• 15. Causal Inference in Social Science Research