Graphical Models and Causal Discovery with Python: 100 Exercises for Building Logic-电子书百科大全

Graphical Models and Causal Discovery with Python: 100 Exercises for Building Logic book cover

Graphical Models and Causal Discovery with Python: 100 Exercises for Building Logic

Author(s): Joe Suzuki (Author)

Publisher: Springer
Publication Date: June 2, 2026
Language: English
Print length: 207 pages
ISBN-10: 9819553075
ISBN-13: 9789819553075

Book Description

Beginning with a gentle introduction to causal discovery and the foundations of probability and statistics, this textbook is written in a highly pedagogical way. By uniting probability theory, statistical inference, and graph theory, the book offers a systematic pathway from foundational principles to cutting-edge algorithms, including independence tests, the PC algorithm, LiNGAM, information criteria, and Bayesian methods. Far more than a theoretical treatment, this volume emphasizes hands-on learning through Python implementations, carefully designed exercises with solutions, and intuitive graphical illustrations. Readers will gain the ability to see, run, and understand causal discovery methods in practice.

Key features of this book include:

A clear and self-contained introduction, bridging probability, statistics, and modern causal discovery techniques
100 exercises with solutions, supporting self-study and classroom use
Reproducible Python code, allowing readers to implement and extend the methods themselves
Intuitive figures and visual explanations that clarify abstract concepts
Broad coverage of applications within statistics and data science, connecting rigorous theory with modern machine learning and causal inference

Editorial Reviews

From the Back Cover

Key features of this book include:

A clear and self-contained introduction, bridging probability, statistics, and modern causal discovery techniques
100 exercises with solutions, supporting self-study and classroom use
Reproducible Python code, allowing readers to implement and extend the methods themselves
Intuitive figures and visual explanations that clarify abstract concepts
Broad coverage of applications within statistics and data science, connecting rigorous theory with modern machine learning and causal inference

About the Author

Joe Suzuki is a professor of statistics at Osaka University, Japan.

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