According to Nancy Cartwright, a causal law holds just when a certain probabilistic condition obtains in all test situations which in turn satisfy a set of background conditions. These background conditions are shown to be inconsistent and, on separate account, logically incoherent. I offer a corrective reformulation which also incorporates a strategy for problems like Hesslow's thrombosis case. I also show that Cartwright's recent argument for modifying the condition to appeal to singular causes fails.Proposed modifications of the theory's probabilistic condition (...) to handle effects with extreme probabilities (0 or 1) are found unsatisfactory. I propose a unified solution which also handles extreme causes. Undefined conditional probabilities give rise to three good, but non-equivalent, ways of formulating the theory. Various formulations appear in the literature. I give arguments to eliminate all but one candidate. Finally, I argue for a crucial new condition clause, and show how to extend the results beyond a simple probabilistic framework. (shrink)

The units of selection problem is an issue within evolutionary theory (or the philosophy thereof) and concerns the question of what units or objects are acted upon by natural selection -- for example, whether these are genes, organisms or groups of organisms. One of the central theses of Elliot Sober’s recent book,The Nature of Selection, is that the philosophical problem of what it means for something to be a unit of selection is to be understood by applying the correct account (...) of what it isin generalfor a factor to be a cause in a population.The account of population-causation that Sober utilizes for this purpose comes from the recent literature on probabilistic causality. This account requires that for C to cause E in a population, aunanimitycondition must be satisfied: C must raise the probability of E in each causal context. (shrink)

A cause may influence its effect via multiple paths. Paradigmatically (Hesslow [1974]), taking birth control pills both decreases one’s risk of thrombosis by preventing pregnancy and increases it by producing a blood chemical. Building on Pearl ([2001]), I explicate the notion of a path-specific effect. Roughly, a path-specific effect of C on E via path P is the degree to which a change in C would change E were they to be transmitted only via P. Facts about such effects may (...) be gleaned from the structural equations commonly used to represent the causal relationships among variables. I contrast my analysis of the Hesslow case with those given by theorists of probabilistic causality, who mistakenly link it to issues of causal heterogeneity, token-causation and indeterminism. The reason probabilistic theories misdiagnose this case is that they pay inadequate attention to the structural relationships among variables. (shrink)

In this paper I wish to argue that counterfactual analyses of causation are inadequate. I believe the counterfactuals that are involved in counterfactual analyses of causation are often false, and thus the theories do not provide an adequate account of causation. This is demonstrated by the presentation of a counterexample to the counterfactual analyses of causation. I then present a unified theory of causation that is based upon probability and counterfactuals. This theory accounts for both deterministic and indeterministic causation, and (...) is not subject to many of the traditional problems facing theories of causation. (shrink)

There are three questions associated with Simpson’s Paradox (SP): (i) Why is SP paradoxical? (ii) What conditions generate SP?, and (iii) What should be done about SP? By developing a logic-based account of SP, it is argued that (i) and (ii) must be divorced from (iii). This account shows that (i) and (ii) have nothing to do with causality, which plays a role only in addressing (iii). A counterexample is also presented against the causal account. Finally, the causal and logic-based (...) approaches are compared by means of an experiment to show that SP is not basically causal. (shrink)

Richard Otte (1985) has recently criticized the resolution of Simpson's 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 Simpson's paradox can arise that Cartwright's theory cannot handle. And Otte develops his own theory of probabilistic causality. I defend Cartwright's solution, and I argue that there are difficulties with the theory of probabilistic causality that Otte proposes.

Simpson's Paradox is introduced and analysed via the mishaps of a researcher who at first falls afoul of the traps Simpson-reversals can set, and then he learns to exploit those traps to advantage. (Note: An error in the treatment of the Sure Thing Principle is corrected in "Simpson's Paradox: A Logically Benign, Empirically Treacherous Hydra").

I shall argue in this paper that extant definitions of causality are incorrect since they do not cohere in the proper way with the concept of a cause of the universe. This lack of coherence is twofold. For some extant definitions of a cause, there are possible instances of the concept of a cause of the universe that do not satisfy the definitions. For these or other extant definitions, there are entities or occurrences that are not instances of the concept (...) of a cause of the universe but that count as causes of the universe according to the definitions. This failure of coherence between extant definitions of causality and the concept of a cause of the universe entails either that there is some other definition of a cause that is correct or that causality is indefinable. I shall suggest in the conclusion of this paper that it is more plausible to believe that causality is indefinable. (shrink)

Support functions $s(h,e)=p(h\backslash e)-p(h)$ are widely used in discussion of explanation, causality and, recently, in connection with the possibility or otherwise of probabilistic induction. With this latter application in view, a rather complete analysis of the variety of support functions, their interrelationships and their "non-deductive" and "inductive" components is presented. With the restriction to two propositions, three variable probabilities are enough to discuss such problems. The analysis is illustrated by graphs, a Venn diagram and by using the Laplace Rule of (...) Succession as an illustrative example. It is concluded that within this framework one cannot prove or disprove the possibility of probabilistic induction. (shrink)

The unanimity theory is an account of property-level causation requiring that causes raise the probability of their effects in specified test situations. Richard Otte (1981) and others have presented counterexamples in which one property is probabilistically sufficient for at least one other property. Given the continuing discussion (e.g., Cartwright 1989; Cartwright and Dupre 1988; Eells 1988a,b), many apparently think that these problems are minor. By considering the impact of Otte's cases on recent versions of the theory, by raising several new (...) examples, and by criticizing natural replies, I argue that the problems for the unanimity theory are severe. (shrink)