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

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Autor:: Yousri El Fattah

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Serie wydawnicze:: Hands-on
Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
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Stron:: 666

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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.

O autorze książki

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.

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Szczegóły książki

Tytuł oryginału:: Causal Inference with Bayesian Networks. Exploring the Practical Applications and Demonstrations of Causal Inference using R and Python
ISBN Ebooka:: 978-18-350-8921-7, 9781835089217
Data wydania ebooka :: 2024-12-31 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: 1

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Serie wydawnicze: Hands-on

Spis treści książki

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

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