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.

We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account.

In this paper we defend the view that the ordinary notions of cause and effect have a direct and essential connection with our ability to intervene in the world as agents.1 This is a well known but rather unpopular philosophical approach to causation, often called the manipulability theory. In the interests of brevity and accuracy, we prefer to call it the agency theory.2 Thus the central thesis of an agency account of causation is something like this: an event A is (...) a cause of a distinct event B just in case bringing about the occurrence of A would be an effective means by which a free agent could bring about the occurrence of B. In our view the unpopularity of the agency approach to causation may be traced to two factors. The first is a failure to appreciate certain distinctive advantages that this approach has over its various rivals. We have drawn attention to some of these advantages elsewhere, and we summarize below. However, the second and more important factor is the influence of a number of stock objections, objections that seem to have persuaded many philosophers that agency accounts face insuperable obstacles. In this paper we want to show that these objections have been vastly overrated. There are four main objections. (shrink)

Why do we represent the world around us using causal generalizations, rather than, say, purely statistical generalizations? Do causal representations contain useful additional information, or are they merely more efficient for inferential purposes? This paper considers the second kind of answer: it investigates some ways in which causal cognition might aid us not because of its expressive power, but because of its organizational power. Three styles of explanation are considered. The first, building on the work of Reichenbach in "The Direction (...) of Time", points to causal representation as especially efficient for predictive purposes in a world containing certain pervasive patterns of conditional independence. The second, inspired by work of Woodward and others, finds causal representation to be an excellent vehicle for representing all-important relations of manipulability. The third, based in part on my own work, locates the importance of causal cognition in the special role it reserves for information about underlying mechanisms. All three varieties of explanation show promise, but particular emphasis is placed on the third. (shrink)

We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficultiesn in the traditional account.