The book is a treatise on some important aspects of causality from epistemological and ontological points of view, particularly chapters 1 through 5 on understanding, defining, and measuring causality. Except for chapters 4 and 5, chapters 1 to 6 are case based. Chapter 6 on making inferences, surveying some old and current approaches, is the most pragmatic. In chapter 7, more tools and methods are provided for comparison purposes. Chapters 8 and 9 introduce more congruence and process-tracing methods, and chapter 10 finally brings everything together.

The authors have mastered the topics presented; they are thorough and go into great detail and clarity when explaining each of the concepts introduced. The book will be accessible for a very large audience, from philosophy students to those in more technical fields who are concerned about the foundations. However, the chapters on inference and comparing different methods (chapters 6 and 7) would have been greatly complemented by an analysis, even if philosophical, of current mathematical approaches to causation such as Bayesian networks and perturbation analysis (which is only partially explored), from the classical probabilistic approach to Judea Pearl’s do-calculus and, more importantly, the recent breakthrough of algorithmic information dynamics (causality based on algorithmic probability); this missing information would make a second edition even more interesting.

In summary, this is a great book that needs to be expanded in the future to incorporate more technical approaches and comparisons.

More reviews about this item: Amazon