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Causal Inference with Bayesian Networks
Causal Inference with Bayesian Networks

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

By Yousri El Fattah
COMING SOON. Publishing in Dec 2024
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Book Dec 2024 666 pages 1st Edition
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Key benefits

  • Gain a firm understanding of Bayesian networks and structured algorithms for probabilistic inference
  • Acquire a comprehensive understanding of graphical models and their applications in causal inference
  • Gain insights into real-world applications of causal models in multiple domains
  • Enhance your coding skills in R and Python through hands-on examples of causal inference

Description

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.

What you will learn

Representation of knowledge with Bayesian networks Interpretation of conditional independence assumptions Interpretation of causality assumptions in graphical models Probabilistic inference with Bayesian networks Causal effect identification and estimation Machine learning methods for causal inference Coding in R and Python for probabilistic and causal inference

Product Details

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Publication date : Dec 31, 2024
Length 666 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781835084984
Category :

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Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
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Product Details


Publication date : Dec 31, 2024
Length 666 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781835084984
Category :

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