<p>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.&#xa0;</p><p>Key features of this book include:</p><ul><li>A clear and self-contained introduction, bridging probability, statistics, and modern causal discovery techniques</li><li>100 exercises with solutions, supporting self-study and classroom use</li><li>Reproducible R code, allowing readers to implement and extend the methods themselves</li><li>Intuitive figures and visual explanations that clarify abstract concepts</li><li>Broad coverage of applications within statistics and data science, connecting rigorous theory with modern machine learning and causal inference</li></ul>