The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

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. 1. Introduction2. Causal models: a review2.1Causal models2.2Syntax and semantics3. The definition of cause4. Examples5. A more refined definition6. DiscussionAAppendix: Some Technical IssuesA.1The active causal processA.2A closer look at AC2(b)A.3Causality with infinitely many variablesA.4Causality in nonrecursive (...) models. (shrink)

Counterfactual analyses of causation can provide elegant analyses of many cases of causation. However, they fail to give intuitively correct analyses of cases involving a commonplace variety of late preemptive causation. I argue that a small emendation can solve the problem.

A theory of the causal relate as aspects or property instances is developed. A supposed problem for transitivity is assessed and then resolved with aspects as the causal relata.

I propose an amendment of Lewis's counterfactual analysis of causation, designed to overcome some difficulties concerning redundant causation.

Following Dretske (1977), there has been a considerable body of literature on the role of contrastive stress in causal claims. Following van Fraassen (1980), there has been a considerable body of literature on the role of contrastive stress in explanations and explanation-requesting why-questions. Amazingly, the two bodies of literature have remained almost entirely disjoint. With an understanding of the contrastive nature of ordinary causal claims, and of the linguistic roles of contrastive stress, it is possible to provide a unified account (...) of both phenomena. I provide such an account from within the framework of a probabilistic theory of causation. Relations of screening-off, long familiar to researchers in probabilistic causality, play a central role in this account. (shrink)

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections (...) and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence and systems from The Cognitive Science Society. (shrink)

Specifying and implementing dynamical systems with the situation calculus.

Thirty years after Lewis's paper, this book brings together some of the most important recent work connecting—or, in some cases, disputing the connection ...

Extant counterfactual accounts of causation (CACs) still cannot handle preemptive causation. I describe a new variety of preemption, defend its possibility, and use it to show the inadequacy of extant CACs. Imagine that it is a law of nature that the first spell cast on a given day match the enchantment that midnight.

As the ball flew towards us I leapt to my left to catch it. But it was you, reacting more rapidly than I, who caught the ball just in front of the point at which my hand was poised. Fortunate for us that you took the catch. The ball was headed on a course which, unimpeded, would have taken it through the glass window of a nearby building. Your catch prevented the window from being broken.

Causes often influence their effects via multiple routes. Moderate alcohol consumption can raise the level of HDL ('good') cholesterol, which in tum reduces the risk of heart disease. Unfortunately, moderate alcohol consumption can also increase the level of homocysteine, which in tum increases the risk of heart disease. The net or overall effect of alcohol consumption on heart disease will depend upon both of these routes, and no doubt upon many others as well. This is a familiar fact of life (...) for engineers and policy makers, one that often gives rise to unintended consequences. Suppose, for example, that the American Federal Aviation Administration were to institute new regulations requiring that aeroplanes be equipped with some expensive new safety feature. Would this regulation save lives? Not necessarily. Every dollar (or pound, or euro, or yen ... ) that an airline spends to upgrade its fleet is a dollar that must be recouped in some way, most likely through higher fares. Higher fares may, in tum, persuade some travellers to drive instead of fly- especially on shorter routes. But it is inherently more dangerous to drive a given route than to fly it, so the net effect of the new regulation may cost lives, rather than saving them (Glassner 1999: 188). (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.