Judea Pearl has been at the forefront of research in the burgeoning field of causal modeling, and Causality is the culmination of his work over the last dozen or so years. For philosophers of science with a serious interest in causal modeling, Causality is simply mandatory reading. Chapter 2, in particular, addresses many of the issues familiar from works such as Causation, Prediction and Search by Peter Spirtes, Clark Glymour, and Richard Scheines. But philosophers with a more general interest in (...) causation will also profit from reading Pearl’s book, especially the material in chapters 7, 9, and 10, which is self-contained and less technical than other parts of the book. The present review is aimed primarily at readers of the second type. (shrink)

This paper presents a method for investigating counterfactual histories of science. A central notion to our theory of science are "advances" (ideas, data, etc.), which are units passed among scientists and which would be conserved in passing from one possible history to another. Advances are connected to each other by nets of causal influence, and we distinguish strong and weak influences. Around sixty types of advances are grouped into ten classes. As our case study, we examine the beginning of the (...) Old Quantum Theory, using a computer to store and process historical information. We describe four plausible possible histories, along with six other implausible ones. (shrink)

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.