Graphical models and causal discovery with Python 100 exercises for building logic Joe Suzuki

Format:

Book

Author/Creator:

Suzuki, Joe, author.

Series:

Oregon State monographs. Mathematics and statistics series

Mathematics and Statistics Series

Language:

English

Subjects (All):

Graphical modeling (Statistics).

Python (Computer program language).

Physical Description:

1 online resource

illustration

Place of Publication:

Singapore Springer 2026

Summary:

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

Contents:

A Gentle Introduction to Causal Discovery

Foundations of Probability and Statistics

Graphical Models

Testing Independence and Conditional Independence with Kernels

The PC Algorithm

LiNGAM

Information Criteria and Marginal Likelihood

Score-Based Structure Learning

Notes:

Includes bibliographical references and index

Online resource; title from PDF title page (SpringerLink, viewed June 22, 2026)

Other Format:

Print version Suzuki, Joe Graphical Models and Causal Discovery with Python

ISBN:

9789819553082

9819553083

OCLC:

1597453127

Access Restriction:

Restricted for use by site license