Causal Inference with Bayesian Networks
Packt Publishing Limited (Verlag)
978-1-83508-498-4 (ISBN)
Key Features
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
Book DescriptionThis 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
Who this book is forThis 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.
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.
Table of Contents
Introduction
Basics of Probability
Bayesian Networks
Structured Causal Models
Relational Database Models
Probabilistic Inference in Bayesian Networks
Probabilistic Inference in Relational Database Models
Causal Inference with Structured Causal Models
Causal Inference with Observational Data
Causal Inference using Machine Learning
Causal Modeling in Factory Automation Diagnostics
Causal Inference in Economic Research
Causal Inference in Epidemiology
Causal Inference in Finance
| Erscheinungsdatum | 19.04.2024 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Technik | |
| ISBN-10 | 1-83508-498-2 / 1835084982 |
| ISBN-13 | 978-1-83508-498-4 / 9781835084984 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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