Ever since David Hume, empiricists have barred powers and capacities from nature. In this book Cartwright argues that capacities are essential in our scientific world, and, contrary to empiricist orthodoxy, that they can meet sufficiently strict demands for testability. Econometrics is one discipline where probabilities are used to measure causal capacities, and the technology of modern physics provides several examples of testing capacities (such as lasers). Cartwright concludes by applying the lessons of the book about capacities and probabilities to the (...) explanation of the role of causality in quantum mechanics. (shrink)

In clinical and agricultural trials, there is the danger that an experimental outcome appears to arise from the causal process or treatment one is interested in when, in reality, it was produced by some extraneous variation in the experimental conditions. The remedy prescribed by classical statisticians involves the procedure of randomization, whose effectiveness and appropriateness is criticized. An alternative, Bayesian analysis of experimental design, is shown, on the other hand, to provide a coherent and intuitively satisfactory solution to the problem.

Urbach (1985) has concluded that the use of randomization in the design of clinical and agricultural trials is both inappropriate and ineffective. It is argued here that it is appropriate, as it eliminates the dependence of inference on the unknown precise physical model that underlies a set of observations, and effective, in that it is relatively simple to apply in practice compared with any competing method. Furthermore, it has been proven in practice.

After clarifying the probabilistic conception of causality suggested by Good (1961-2), Suppes (1970), Cartwright (1979), and Skyrms (1980), we prove a sufficient condition for transitivity of causal chains. The bearing of these considerations on the units of selection problem in evolutionary theory and on the Newcomb paradox in decision theory is then discussed.

John Dupré (1984) has recently criticized the theory of probabilistic causality developed by, among others, Good (1961-62), Suppes (1970), Cartwright (1979), and Skyrms (1980). He argues that there is a tension or incompatibility between one of its central requirements for the presence of a causal connection, on the one hand, and a feature of the theory pointed out by Elliott Sober and me (1983), on the other. He also argues that the requirement just alluded to should be given up. I (...) defend the theory against Dupré's criticisms and conclude with comments on Dupré's appraisal of the bearing of his arguments on the nature of probabilistic causal laws. (shrink)

In an earlier paper (Dupré 1984), I criticized a thesis sometimes defended by theorists of probabilistic causality, namely, that a probabilistic cause must raise the probability of its effect in every possible set of causally relevant background conditions (the "contextual unanimity thesis"). I also suggested that a more promising analysis of probabilistic causality might be sought in terms of statistical relevance in a fair sample. Ellery Eells (1987) has defended the contextual unanimity thesis against my objections, and also raised objections (...) of his own to my positive claims. In this paper I defend and amplify both my objections to the contextual unanimity thesis and my constructive suggestion. (shrink)