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

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 techniques100 exercises with solutions, supporting self-study and classroom useReproducible R code, allowing readers to implement and extend the methods