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Full 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 R 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 R 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.
