Causal modeling methods such as path analysis, used in the social and natural sciences, are also highly relevant to philosophical problems of probabilistic causation and statistical explanation. We show how these methods can be effectively used (1) to improve and extend Salmon's S-R basis for statistical explanation, and (2) to repair Cartwright's resolution of Simpson's paradox, clarifying the relationship between statistical and causal claims.

The key problem in the controversy over group selection is that of defining a criterion of group selection that identifies a distinct causal process that is irreducible to the causal process of individual selection. We aim to clarify this problem and to formulate an adequate model of irreducible group selection. We distinguish two types of group selection models, labeling them type I and type II models. Type I models are invoked to explain differences among groups in their respective rates of production of contained individuals. Type II models are invoked to explain differences among groups in their respective rates of production of distinct new groups. Taking Elliott Sober's model as an exemplar, we argue that although type I models have some biological importance—they force biologists to consider the role of group properties in influencing the fitness of organisms—they fail to identify a distinct group-level causal selection process. Type II models if properly framed, however, do identify a group-level causal selection process that is not reducible to individual selection. We propose such a type II model and apply it to some of the major candidates for group selection.

Horwich argues that we should reject metaphysical realism, but that we can preserve semantic realism by adhering to a redundancy theory of truth and a confirmationist account of linguistic understanding. But the latter will give us semantic realism only if it allows that the truth-values of sentences may transcend our recognitional capacities, and this is possible only insofar as we covertly reintroduce metaphysical realism. In spite of its intuitive appeal, we should not endorse semantic realism, but this need not bear upon the tenability of scientific realism.

Two EPR arguments are reviewed, for their own sake, and for the purpose of clarifying the status of “stochastic“ hidden variables. The first is a streamlined version of the EPR argument for the incompleteness of quantum mechanics. The role of an anti-instrumentalist (“realist”) interpretation of certain probability statements is emphasized. The second traces out one horn of a central foundational dilemma, the collapse dilemma; complex modal reasoning, similar to the original EPR, is used to derive determinateness (of all spin components of two spin-½ particles in the singlet state) from just (a form of) weak locality, result definiteness, and an assumption on propensities based on conservation. Theories meeting these conditions are therefore constrained by the Bell inequalities. Neither controversial assumptions of “strong locality” (“factorability”) nor of determinism are employed in the derivation. The categories of “stochastic hidden variables” are then analyzed; one can focus on “quasi-definite” theories, without loss of generality. A means of excluding these is proposed, based on a demand that certain ideal cases be accurately treated. Theorems from quantum measurement theory, sometimes cited as showing that such cases are not physically possible, are found inapplicable.

A Quantum-Mechanical automaton, equipped with mechanisms for the measurement and the recording and the prediction of certain physical properties of the world, is described. It is inquired what sort of empirical description such an automaton would produce of itself. It turns out that this description would be a very novel one, one such as was never imagined in the conventional discussions of measurement.

Isaac Levi and I have different views of probability and decision making. Here, without addressing the merits, I will try to answer some questions recently asked by Levi (1985) about what my view is, and how it relates to his.

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.

If the decades of the forties through the sixties were dominated by discussion of Hempel's “covering law“ explication of explanation (and its variants), that of the seventies was preoccupied with Salmon's “statistical relevance” conception, which emerged as the principal alternative to Hempel's enormously influential account. Readers of Wesley C. Salmon's Scientific Explanation and the Causal Structure of the World (Salmon 1984), therefore, ought to find it refreshing to discover that its author has not remained content with a facile defense of his previous investigations; on the contrary, Salmon offers an original account of different kinds of explications, advances additional criticisms of various alternative theories, and elaborates a novel “two-tiered“ analysis of explanation that tacitly depends upon a “two-tiered” account of homogeneity. Indeed, if the considerations that follow are correct, Salmon has not merely refined his statistical relevance account but has actually abandoned it in favor of a “causal/mechanistic“ construction. This striking development suggests that the theory of explanation is likely to remain as lively an arena of debate in the eighties as it has been in the past.