Probabilistic Causation

A key challenge for probabilistic causal models is to distinguish non-causal probabilistic dependencies from true causal relations. To accomplish this task, causal models are usually required to satisfy several constraints. Two prominent constraints are the causal Markov condition and the faithfulness condition. However, other constraints are also needed. One of these additional constraints is the causal sufficiency condition, which states that models must not omit any direct common causes of the variables they contain. In this paper, I argue that the (...) causal sufficiency condition is problematic: (1) it is incompatible with the requirement that the variables in a model must not stand in non-causal necessary dependence relations, such as mathematical or conceptual relations, or relations described in terms of supervenience or grounding, (2) it presupposes more causal knowledge as primitive than is actually needed to create adequate causal models, and (3) if models are only required to be causally sufficient, they cannot deal with cases where variables are probabilistically related by accident, such as Sober’s example of the relationship between bread prices in England and the sea level in Venice. I show that these problems can be avoided if causal models are required to be monotonic in the following sense: the causal relations occurring in a model M would not disappear if further variables were added to M. I give a definition of this monotonicity condition and conclude that causal models should be required to be monotonic rather than causally sufficient. (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)

Sober [2011] argues that some causal statements are a priori true and that a priori causal truths are central to explanations in the theory of natural selection. Lange and Rosenberg [2011] criticize Sober's argument. They concede that there are a priori causal truths, but maintain that those truths are only ‘minimally causal’. They also argue that explanations that are built around a priori causal truths are not causal explanations, properly speaking. Here we criticize both of Lange and Rosenberg's claims.

The starting point in the development of probabilistic analyses of token causation has usually been the naïve intuition that, in some relevant sense, a cause raises the probability of its effect. But there are well-known examples both of non-probability-raising causation and of probability-raising non-causation. Sophisticated extant probabilistic analyses treat many such cases correctly, but only at the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-at-a-distance, prevention, and causation by absence and omission. I show that (...) an examination of the structure of these problem cases suggests a different treatment, one which avoids the costs of extant probabilistic analyses. (shrink)

A definition of causation as probability-raising is threatened by two kinds of counterexample: first, when a cause lowers the probability of its effect; and second, when the probability of an effect is raised by a non-cause. In this paper, I present an account that deals successfully with problem cases of both these kinds. In doing so, I also explore some novel implications of incorporating into the metaphysical investigation considerations of causal psychology.

Scepticism concerning the idea of causation being linked to contingent chance-raising is articulated in Beebee’s challenging chapter. She suggests that none of these approaches will avoid the consequence that spraying defoliant on a weed is a cause of the weed’s subsequent health. We will always be able to abstract away enough of the healthy plant processes so all that’s left is the causal chain involving defoliation and health. In those circumstances, there will be contingent chance-raising. Beebee’s conclusion is that we (...) should reject the idea of contingent chance-raising and just accept that all causation involves chance-raising. This involves the reclassification of some intuitive cases of causation as causal processes without causation but rather hindering (a distinctive kind of process). It seems clear from this discussion and from the brief earlier remarks about the status of prevention that the classification of types of causal processes and the characterization of their link to causation are matters of some importance. (shrink)

Mellor´s theory of causation has two components, one according to which causes raise their effects´ chances, and one according to which causation links facts. I argue that these two components are not independent from each other and, in particular, that Mellor´s thesis that causation links facts requires his thesis that causes raise their effects´ chances, since without the latter thesis Mellor cannot stop the slingshot argument, an argument that is a threat to any theory postulating facts as the relata of (...) causation. (shrink)

This paper examines a promising probabilistic theory of singular causation developed by David Lewis. I argue that Lewis' theory must be made more sophisticated to deal with certain counterexamples involving pre-emption. These counterexamples appear to show that in the usual case singular causation requires an unbroken causal process to link cause with effect. I propose a new probabilistic account of singular causation, within the framework developed by Lewis, which captures this intuition.

There are two concepts of causes, property causation and token causation. The principle I want to discuss describes an epistemological connection between the two concepts, which I call the Connecting Principle. The rough idea is that if a token event of type Cis followed by a token event of type E, then the support of the hypothesis that the first event token caused the second increases as the strength of the property causal relation of C to E does. I demonstrate (...) the principle, illustrate its application to phylogenies, infections, and rumours, and discuss its consequence for the conceptual distinctness of causal processes from the events they connect. Although I am by no means confident that the Connecting Principle is ultimately correct, it seems to be a useful point of departure into an important aspect of the epistemology of causality. (shrink)

This article introduces the notion of the directedness of a process, which underlies event causation as well as the persistence of things. Using this notion it investigates what happens in typical cases of active event causation. Causes never necessitate their effects because even non- probabilistic causes can be counteracted.