Ellery Eells

Probabilistic Causality and the Question of Transitivity

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.

Symmetries and Asymmetries in Evidential Support

Several forms of symmetry in degrees of evidential support areconsidered. Some of these symmetries are shown not to hold in general. This has implications for the adequacy of many measures of degree ofevidential support that have been proposed and defended in the philosophical literature.

Metatickles and the dynamics of deliberation

The traditional or “orthodox” decision rule of maximizing conditional expected utility has recently come under attack by critics who advance alternative “causal” decision theories. The traditional theory has, however, been defended. And these defenses have in turn been criticized. Here, I examine two objections to such defenses and advance a theory about the dynamics of deliberation (a diachronic theory about the process of deliberation) within the framework of which both objections to the defenses of the traditional theory fail.

Probabilistic Causality: Reply to John Dupré

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 Duprés criticisms and conclude with comments on Duprés appraisal of the bearing of his arguments on the nature of probabilistic causal laws.

Objective probability theory theory

I argue that to the extent to which philosophical theories of objective probability have offered theoretically adequateconceptions of objective probability (in connection with such desiderata as causal and explanatory significance, applicability to single cases, etc.), they have failed to satisfy amethodological standard — roughly, a requirement to the effect that the conception offered be specified with the precision appropriate for a physical interpretation of an abstract formal calculus and be fully explicated in terms of concepts, objects or phenomena understood independently of the idea of physical probability. The significance of this, and of the suggested methodological standard, is then briefly discussed.

Common Causes and Decision Theory

One of us (Eells 1982) has defended traditional evidential decision theory against prima facie Newcomb counterexamples by assuming that a common cause forms a conjunctive fork with its joint effects. In this paper, the evidential theory is defended without this assumption. The suggested rationale shows that the theorys assumptions are not about the nature of causality, but about the nature of rational deliberation. These presuppositions are weak enough for the argument to count as a strong justification of the evidential theory.

Newcomb's many solutions

After a brief presentation and discussion of two versions of Newcombs problem, I examine the analyses proposed by Levi (1975). Horgan (1981), and Kyburg (1980). I argue that the first two are not genuine solutions to the problem, but that the third, if appropriately elaborated and modified, is correct.

Probabilistic Causal Interaction and Disjunctive Causal Factors

The basic idea in probabilistic theories of causality is that causes raise the probability of their effects. Of course, it is necessary to control for other causes of the effect in question in order to avoid being misled by “spurious correlations”, which arise in cases of “Simpson’s paradox”. But, even when we control for other causes, it is possible for a causal factor to raise the probability of a second factor in some situations and lower that probability in other situations, where these situations do not correspond to the presence or absence of other causes of the second factor. When this happens, the reason may be that the causal factor “interacts” with other factors. In this paper, I will explore the phenomenon of probabilistic causal interaction in detail and in general, and I will suggest a general way of accommodating the possibility of causal interaction in one common current understanding of probabilistic causation.

Probabilistic Causal Levels

According to a standard theory of probabilistic causality, causes C raise the probability of their effects E within causally homogeneous background contexts. More precisely, let K i ’s be maximal conjunctions of factors — not including C, not-C, or effects thereof — that are causally relevant to E. Then C causes E if and only if, for each i, Pr(E/K i C the issues relevant to which of the two versions is more plausible are irrelevant to the issues I wish to discuss, and this will simplify my discussion.

Cartwright and Otte on Simpson's Paradox

Richard Otte (1985) has recently criticized the resolution of Simpsons paradox given by Nancy Cartwright (1979). He argues that there are difficulties with the version of the theory of probabilistic causality that Cartwright has developed, and that there is a way in which Simpsons paradox can arise that Cartwrights theory cannot handle. And Otte develops his own theory of probabilistic causality. I defend Cartwrights solution, and I argue that there are difficulties with the theory of probabilistic causality that Otte proposes.