This paper outlines a general theory of efficient causation, a theory that deals in a unified way with traditional or deterministic, indeterministic, probabilistic, and other causal concepts. Theorists like Lewis, Salmon, and Suppes have attempted to broaden our causal perspective by reductively analysing causal notions in other terms. By contrast, the present theory rests in the first place on a non-reductive analysis of traditional causal concepts — into formal or structural components, on the one hand, and a physical or metaphysical (...) component, on the other. The analyzans is then generalised. The theory also affords a more general propensity notion than is standard, one that helps solve major problems facing propensity interpretations of probability. (shrink)

What has become generally known as the Berlin School of Logical Empiricism constitutes a philosophical movement that was erected on foundations laid by Albert Einstein. His revolutionary work in physics had a profound impact on philosophers interested in scientific issues, prominent among them Paul Oppenheim and Hans Reichenbach, the founding fathers of the school, who joined in viewing him as their hero among philosopher-scientists. Overall the membership of this school falls into three groups. The founding generation was linked by the (...) circumstance that both Grelling and Reichenbach were collaborators of Oppenheim; the middle generation by the fact that both Hempel and Helmer were students of Reichenbach's in Berlin; and the younger generation by the fact that all of its members were students and disciples either of Reichenbach's or of Hempel's in the USA. Three stages are at issue: an initial phase in Berlin, a transatlantic migration, and a continuation in the U.S.A.--principally in Pittsburgh. (shrink)

This essay presents a model-theoretic account of dispositional properties, according to which dispositions are not ordinary properties of real entities; dispositions capture the behavior of abstract, idealized models. This account has several payoffs. First, it saves the simple conditional analysis of dispositions. Second, it preserves the general connection between dispositions and regularities, despite the fact that some dispositions are not grounded in actual regularities. Finally, it brings together the analysis and the explanation of dispositions under a unified framework.

The Principle of the Common Cause is usually understood to provide causal explanations for probabilistic correlations obtaining between causally unrelated events. In this study, an extended interpretation of the principle is proposed, according to which common causes should be invoked to explain positive correlations whose values depart from the ones that one would expect to obtain in accordance to her probabilistic expectations. In addition, a probabilistic model for common causes is tailored which satisfies the generalized version of the principle, at (...) the same time including the standard conjunctive-fork model as a special case. (shrink)

The connection between the probabilities of conditionals and the corresponding conditional probabilities has long been explored in the philosophical literature, but its implementation faces both technical obstacles and objections on empirical grounds. In this paper I ?rst outline the motivation for the probabilistic turn and Lewis’ triviality results, which stand in the way of what would seem to be its most straightforward implementation. I then focus on Richard Jeffrey’s ’random-variable’ approach, which circumvents these problems by giving up the notion that (...) conditionals denote propositions in the usual sense. Even so, however, the random-variable approach makes counterintuitive predictions in simple cases of embedded conditionals. I propose to address this problem by enriching the model with an explicit representation of causal dependencies. The addition of such causal information not only remedies the shortcomings of Jeffrey’s conditional, but also opens up the possibility of a uni?ed probabilistic account of indicative and counterfactual conditionals. (shrink)

I advance a new theory of causal relevance, according to which causal claims convey information about conditional probability functions. This theory is motivated by the problem of disjunctive factors, which haunts existing probabilistic theories of causation. After some introductory remarks, I present in Section 3 a sketch of Eells's (1991) probabilistic theory of causation, which provides the framework for much of the discussion. Section 4 explains how the problem of disjunctive factors arises within this framework. After rejecting three proposed solutions, (...) I offer in Section 6 a new approach to causation that avoids the problem. Decision-theoretic considerations also support the new approach. Section 8 develops the consequences of the new theory for causal explanation. The resulting theory of causal explanation incorporates the new insights while respecting important work on scientific explanation by Salmon (1971), Railton (1981), and Humphreys (1989). My conclusions are enumerated in Section 9. (shrink)

The notion of common cause closedness of a classical, Kolmogorovian probability space with respect to a causal independence relation between the random events is defined, and propositions are presented that characterize common cause closedness for specific probability spaces. It is proved in particular that no probability space with a finite number of random events can contain common causes of all the correlations it predicts; however, it is demonstrated that probability spaces even with a finite number of random events can be (...) common cause closed with respect to a causal independence relation that is stronger than logical independence. Furthermore it is shown that infinite, atomless probability spaces are always common cause closed in the strongest possible sense. Open problems concerning common cause closedness are formulated and the results are interpreted from the perspective of Reichenbach's Common Cause Principle. (shrink)

The causal theory of reasons holds that acting for a reason entails that the agents action was caused by his or her beliefs and desires. While Donald Davidson (1963) and others effectively silenced the first objections to the theory, a new round has emerged. The most important recent attack is presented by Jonathan Dancy in Practical Reality (2000) and subsequent work. This paper will defend the causal theory against Dancy and others, including Schueler (1995), Stoutland (1999, 2001), and Ginet (2002).Dancy (...) observes that our reasons are neither psychological states nor causes, and that our reasons can be both motivating and normative. I argue that these observations are fully compatible with the causal theory. According to the reductive version I develop for both cognitive and optative reasons, what it is for an action to be done for a reason is for certain beliefs and desires to cause the action in a particular way. Our reasons for action are the objects of some of those beliefs and desires. The causal process has two stages. This theory explains not only Dancys observations, but also many other facts about reasons that alternative theories leave unexplained. I argue against Schueler and others that the non-appetitive desires entailed by acting for reasons are no less distinct and independent causal factors than the beliefs entailed. I go on to rebut arguments that the relation between psychological states and actions cannot be causal because it is non-empirical, rational, normative, or non-deterministic, and that explanations in terms of psychological causes are incompatible with explanations in terms of reasons. (shrink)

In order to comprehend the world around us and construct explaining theories for this purpose, we need a conception of physical probability, since we come across many (apparently) probabilistic phenomena in our world. But how should we understand objective probability claims? Since pure frequency approaches of probability are not appropriate, we have to use a single case propensity interpretation. Unfortunately, many philosophers believe that this understanding of probability is burdened with significant difficulties. My main aim is to show that we (...) can treat propensity as a theoretical concept that exhibits many similarities to other theoretical concepts, and its difficulties are not insuperable if we make explicit some general presuppositions of scientific practice and apply them to propensities. At least this is true if we formulate the right bridge principle for propensity and rely on further methodological rules in dealing with propensity assertions to make them empirically testable. (shrink)

In an influential article published in 1982, Bas Van Fraassen developed an argument against causal realism on the basis of an analysis of the Einstein-Podolsky-Rosen correlations of quantum mechanics. Several philosophers of science and experts in causal inference -including some causal realists like Wesley Salmon- have accepted Van Fraassen’s argument, interpreting it as a proof that the quantum correlations cannot be given any causal model. In this paper I argue that Van Fraassen’s article can also be interpreted as a good (...) guide to the different causal models available for the EPR correlations, and their relative virtues. These models in turn give us insight into some of the unusual features that quantum propensicies might have. (shrink)