In a recent article in this journal, Federica Russo and Jon Williamson argue that an analysis of causality in terms of probabilistic relationships does not do justice to the use of mechanistic evidence to support causal claims. I will present Ronald Giere's theory of probabilistic causation, and show that it can account for the use of mechanistic evidence (both in the health sciences—on which Russo and Williamson focus—and elsewhere). I also review some other probabilistic theories of causation (of Suppes, Eells, (...) and Humphreys) and show that they cannot account for the use of mechanistic evidence. I argue that these theories are also inferior to Giere's theory in other respects. (shrink)

In a recent article (Sober 1982), I criticized an account of causation proposed by Giere (1979, 1980) by describing a series of examples concerning natural selection. Collier (1983) has criticized my criticisms, saying that I misapplied Giere's proposal and misconstrued the biology. More recently, Giere (1984) has defended his theory against my criticisms. Here I argue that my criticisms still stand.

Russell (1948), Reichenbach (1956), and Salmon (1975, 1979) have argued that a fundamental principle of science and common sense is that "matching" events should not be chalked up to coincidence, but should be explained by postulating a common cause. Reichenbach and Salmon provided this intuitive idea with a probabilistic formulation, which Salmon used to argue for a version of scientific realism. Van Fraassen (1980, 1982) showed that the principle, so construed, runs afoul of certain results in quantum mechanics. In this (...) paper a new formulation of the principle is offered that emerges from its use in evolutionary theory. This characterization identifies fairly general conditions in which postulating common causes will be more explanatory than postulating separate causes. (shrink)

In a recent discussion note Sober (1985) elaborates on the argument given in Sober (1982) to show the inadequacy of Ronald Giere's (1979, 1980) causal model for cases of frequency-dependent causation, and denies that Giere's (1984) response avoids the problem he raises. I argue that frequency-dependent effects do not pose a problem for Giere's original causal model, and that all parties in this dispute have been guity of misinterpreting the counterfactual populations involved in applying Giere's model.

The purpose of this essay is to investigate the properties of singular causal systems and their population manifestations, with special concern for the thesis of methodological individualism, which claims that there are no properties of social groups that cannot be adequately explained exclusively by reference to properties of individual members of those groups, i.e., at the level of individuals. Individuals, however, may be viewed as singular causal systems, i.e., as instantiations of (arrangements of) dispositional properties. From this perspective, methodological individualism (...) appears to be an ambiguous thesis: some properties of collections of (independent) systems of the same kind are reducible, but other properties of collections of (dependent) systems of the same kind are not. In cases of the first kind, therefore, methodological individualism is true, but trivial; while in cases of the second kind, it is significant, but false. Hence, if the arguments that follow are correct, at least some of the properties of social groups should qualify as emergent. (shrink)

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)

It is possible for a causal factor to raise the probability of a second factor in some situations while lowering the probability of the second factor in other situations. Must a genuine cause always raise the probability of a genuine effect of it? When it does not always do so, an "interaction" with some third factor may be the reason. I discuss causal interaction from the perspectives of Giere's counterfactual characterization of probabilistic causal connection (1979, 1980) and the "contextual unanimity" (...) model developed by, among others, Cartwright (1979) and Skyrms (1980). I argue that the contextual unanimity theory must exercise care, in a new way that seems to have gone unnoticed, in order to adequately accommodate the phenomenon, and that the counterfactual theory must be substantially revised; although it will still, pending clarification of a second kind of revision, be unable to accommodate a kind of interaction exemplified in cases like those described by Sober (1982). (shrink)

Darwinians are realists about the force of selection, but there has been surprisingly little discussion about what form this realism should take. Arguments about the units of selection in general and genic selectionism in particular reveal two realist assumptions: (1) for any selection process, there is a uniquely correct identification of the operative selective forces and the level at which each impinges; and (2) selective forces must satisfy the Pareto-style requirement of probabilistic causation. I argue that both assumptions are false; (...) we must temper realism about the force of selection and revise the way we think about probabilistic causation. (shrink)

After briefly presenting Ronald Giere's (1979, 1980) recent counterfactual characterization of population-level causation, I present two counterexamples to the characterization. The difficulty discussed stems from nonaccidental correlations that can obtain between causally effective and causally neutral factors.

Ronald Giere's analysis of causal effectiveness in populations involves the comparison of two hypothetical populations, one in which every individual has the suspected causal factor, and the other in which none do. Elliott Sober has argued that in cases where causal effectiveness depends on relative population sizes, Giere's analysis breaks down. I take issue with this claim, and argue to the contrary that Giere's analysis can help provide insight into these cases.