The book "Causation, Prediction, and Search" by Peter Spirtes, Clark Glymour, and Richard Scheines is a comprehensive treatise on causal inference and its applications in statistics. The authors aim to bridge the gap between theoretical statistics and practical applications, particularly in fields such as social sciences, epidemiology, economics, and engineering. They argue that causal inference is a crucial but often neglected aspect of statistics, and they develop a rigorous theory to address this gap. The book begins by motivating the need for causal inference in statistical practice, highlighting the limitations of traditional statistical methods that rely solely on observational data. It introduces the axioms of causal inference, which are essential for understanding how causal structures can be inferred from probability distributions. The authors then explore the consequences of these axioms, including the manipulation theorem and d-separation, which are key tools for causal inference. The book delves into the discovery of causal relationships using algorithms, such as the Wermuth-Lauritzen algorithm and the PC algorithm, and discusses the reliability and statistical limitations of these methods. It also addresses the prediction of causal effects and the design of empirical studies, emphasizing the importance of both experimental and observational data. Key topics include the use of directed graphical models, the role of unmeasured variables, and the comparison of causal and non-causal models. The authors provide numerous examples and simulations to illustrate their theoretical results and algorithms, making the book accessible to both researchers and practitioners. The book concludes with a discussion of open problems and future directions, emphasizing the ongoing need for rigorous and principled approaches to causal inference. It is intended for a broad audience, including statisticians, philosophers, and researchers in various scientific disciplines.The book "Causation, Prediction, and Search" by Peter Spirtes, Clark Glymour, and Richard Scheines is a comprehensive treatise on causal inference and its applications in statistics. The authors aim to bridge the gap between theoretical statistics and practical applications, particularly in fields such as social sciences, epidemiology, economics, and engineering. They argue that causal inference is a crucial but often neglected aspect of statistics, and they develop a rigorous theory to address this gap. The book begins by motivating the need for causal inference in statistical practice, highlighting the limitations of traditional statistical methods that rely solely on observational data. It introduces the axioms of causal inference, which are essential for understanding how causal structures can be inferred from probability distributions. The authors then explore the consequences of these axioms, including the manipulation theorem and d-separation, which are key tools for causal inference. The book delves into the discovery of causal relationships using algorithms, such as the Wermuth-Lauritzen algorithm and the PC algorithm, and discusses the reliability and statistical limitations of these methods. It also addresses the prediction of causal effects and the design of empirical studies, emphasizing the importance of both experimental and observational data. Key topics include the use of directed graphical models, the role of unmeasured variables, and the comparison of causal and non-causal models. The authors provide numerous examples and simulations to illustrate their theoretical results and algorithms, making the book accessible to both researchers and practitioners. The book concludes with a discussion of open problems and future directions, emphasizing the ongoing need for rigorous and principled approaches to causal inference. It is intended for a broad audience, including statisticians, philosophers, and researchers in various scientific disciplines.