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.

1 Background for the Uninitiated RICHMOND CAMPBELL Paradoxes are intrinsically fascinating. They are also distinctively ...

Newcomb’s problem is a decision puzzle whose difficulty and interest stem from the fact that the possible outcomes are probabilistically dependent on, yet causally independent of, the agent’s options. The problem is named for its inventor, the physicist William Newcomb, but first appeared in print in a 1969 paper by Robert Nozick [12]. Closely related to, though less well-known than, the Prisoners’ Dilemma, it has been the subject of intense debate in the philosophical literature. After three decades, the issues remain (...) unresolved. Newcomb’s problem is of genuine importance because it poses a challenge to the theoretical adequacy of orthodox Bayesian decision theory. It has led both to the development of causal decision theory and to efforts aimed at defending the adequacy of the orthodox theory. (shrink)

The Newcomb problem is analysed here as a type of common cause problem. In relation to such problems, if you take the dominated option your expected outcome will be good and if you take the dominant option your expected outcome will be not so good. As is explained, however, these arenot conventional conditional expected outcomes but `conditional evidence expected outcomes' and while in the deliberation process, the evidence on which they are based is only hypothetical evidence.Conventional conditional expected outcomes are (...) more sensitive to your current epistemic state in that they are based purely on actual evidence which is available to you during the deliberation process. So although they are conditional on a certain act being performed, they are not based on evidence that you would have only if that act is performed. Moreover, for any given epistemic state during the deliberation process, your conventional conditional expected outcome for the dominant option will be better than that for the dominated option. The principle of dominance is thus in perfect harmony with the conventional conditional expected outcomes. In relation to the Newcomb problem then, the evidence unequivocally supports two-boxing as the rational option. Yet what is advanced here is not simply a two-boxing strategy. To see why, two stages to the problem need to be recognised. The first stage is that which occurs before the information used by the predictor in making his predictions has been gained. The second stage is after this point. Provided that you are still in the first stage, you have an opportunity to influence whether or not the predictor places the $1m in the opaque box. To maximise the probability that it is, you need to commit yourself to one-boxing. (shrink)

In their development of causal decision theory, Allan Gibbard and William Harper advocate a particular method for calculating the expected utility of an action, a method based upon the probabilities of certain counterfactuals. Gibbard and Harper then employ their method to support a two-box solution to Newcomb’s paradox. This paper argues against some of Gibbard and Harper’s key claims concerning the truth-values and probabilities of counterfactuals involved in expected utility calculations, thereby disputing their analysis of Newcomb’s Paradox. If we are (...) right, then Gibbard and Harper’s method of calculating expected utility does not adequately represent rational choice. (shrink)