Online research in philosophy

I defend what may loosely be called an eliminativist account of causation by showing how several of the main features of causation, namely asymmetry, transitivity, and necessitation (or sometimes probability-raising), arise from the combination of fundamental dynamical laws and a special constraint on the macroscopic structure of matter in the past. At the microscopic level, the causal features of necessitation and transitivity are grounded, but not the asymmetry. At the coarse-grained level of the macroscopic physics, the causal asymmetry is grounded, but not the necessitation or transitivity. Thus, at no single level of description does the physics justify the conditions that are taken to be constitutive of causation. Nevertheless, if we mix our reasoning about the microscopic and macroscopic descriptions, the structure provided by the dynamics and special initial conditions can justify the folk concept of causation to a significant extent. I explain why our causal concept works so well even though at bottom it is comprised of a patchwork of principles that don't mesh well.

In this paper I offer an 'integrating account' of singular causation, where the term 'integrating' refers to the following program for analysing causation. There are two intuitions about causation, both of which face serious counterexamples when used as the basis for an analysis of causation. The 'process' intuition, which says that causes and effects are linked by concrete processes, runs into trouble with cases of 'misconnections', where an event which serves to prevent another fails to do so on a particular occasion and yet the two events are linked by causal processes. The chance raising intuition, according to which causes raise the chance of their effects, easily accounts for misconnections but faces the problem of chance lowering causes, a problem easily accounted for by the process approach. The integrating program attempts to provide an analysis of singular causation by synthesising the two insights, so as to solve both problems. In this paper I show that extant versions of the integrating program due to Eells, Lewis, and Menzies fail to account for the chance-lowering counterexample. I offer a new diagnosis of the chance lowering case, and use that as a basis for an integrating account of causation which does solve both cases. In doing so, I accept various assumptions of the integrating program, in particular that there are no other problems with these two approaches. As an example of the process account, I focus on the recent CQ theory of Wesley Salmon (1997).

The paper builds on the basically Humean idea that A is a cause of B iff A and B both occur, A precedes B, and A raises the metaphysical or epistemic status of B given the obtaining circumstances. It argues that in pursuit of a theory of deterministic causation this ‘status raising’ is best explicated not in regularity or counterfactual terms, but in terms of ranking functions. On this basis, it constructs a rigorous theory of deterministic causation that successfully deals with cases of overdetermination and pre-emption. It finally indicates how the account's profound epistemic relativization induced by ranking theory can be undone. Introduction Variables, propositions, time Induction first Causation Redundant causation Objectivization.

I argue that one central aspect of the epistemology of causation, the use of causes as evidence for their effects, is largely independent of the metaphysics of causation. In particular, I use the formalism of Bayesian causal graphs to factor the incremental evidential impact of a cause for its effect into a direct cause-to-effect component and a backtracking component. While the “backtracking” evidence that causes provide about earlier events often obscures things, once we our restrict attention to the cause-to-effect component it is true to say promoting (inhibiting) causes raise (lower) the probabilities of their effects. This factoring assumes the same form whether causation is given an interventionist, counterfactual or probabilistic interpretation. Whether we think about causation in terms of interventions and causal graphs, counterfactuals and imaging functions, or probability raising against the background of causally homogenous partitions, if we describe the essential features of a situation correctly then the incremental evidence that a cause provides for its effect in virtue of being its cause will be the same.

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.

Schaffer proposes a new account of probabilistic causation that synthesizes the probability-raising and process-linkage views on causation. The driving idea of Schaffer's account is that, although an effect does not invariably depend on its cause, a process linked to the effect does. In this paper, however, I will advance counterexamples to Schaffer's account and then demonstrate that Schaffer's possible responses to them do not work. Finally, I will argue that my counterexamples suggest that the driving idea of Schaffer's account is misdirected.

The traditional model and the contextual unanimity model are two probabilistic accounts of general causation subject to many well-known problems; e.g. cases of epiphenomena, causes raising their own probability, effects raising the probability of the cause, et cetera. After reviewing these problems and raising a new problem for the two models, I suggest the beginnings of an alternative probabilistic account. My suggestion avoids the problems encountered by earlier models, in large part, by an appeal to singular causation.

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.

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.