Cause and Chance is a collection of specially written papers by world-class metaphysicians. Its focus is the problems facing the "reductionist" approach to causation: the attempt to cover all types of causation, deterministic and indeterministic, with one basic theory.

Mellor's subject is singular causation between facts, expressed ‘E because C’. His central requirement for causation is that the chance that E if C be greater than the chance that E if C: chc(E)>chc(E). The book is as much about chance as it is about causation. I show that his way of distinguishing chc (E) from the traditional notion of conditional chance leaves than him with a problem about the existence of chQ(P) when Q is false (Section 3); and also (...) that any notion of chance which conforms to the standard calculus has wider application than the causal instances to which Mellor's notion is restricted (Section 8). Other topics discussed may be gleaned from the headings below. 1 Review of D.H. Mellor [1995]: The Facss of Causation, London, Routledge, International Library of Philosophy. (shrink)

During the past decades several philosophers of science and social scientists have been interested in the problems of causation. Recently attention has been given to probabilistic causation in dichotomous causal systems. The paper uses the basic features of probabilistic causation to argue that the causal modeling approaches developed by such researchers as Blalock (1964) and Duncan (1975) can provide, when an additional assumption is added, adequate qualitative measures of one variable causal influence upon another. Finally, some of the difficulties and (...) issues involved in developing adequate quantitative measures are discussed, and it is concluded that the causal modeling measures cannot provide adequate quantitative measures. (shrink)

I argue that the orthodox account of probabilistic causation, on which probabilistic causes determine the probability of their effects, is inconsistent with certain ontological assumptions implicit in scientific practice. In particular, scientists recognize the possibility that properties of populations can cause the behavior of members of the populations. Such emergent population‐level causation is metaphysically impossible on the orthodoxy.

I argue that the orthodox account of probabilistic causation, on which probabilistic causes determine the probability of their effects, is inconsistent with certain ontological assumptions implicit in scientific practice. In particular, scientists recognize the possibility that properties of populations can cause the behavior of members of the populations. Such emergent population level causation is metaphysically impossible on the orthodoxy.

A probabilistic theory of causation is a theory which holds that the central feature of causation is that causes (usually) raise the probability of their effects. In this dissertation, I defend Hans Reichenbach's original (1953) version of the probabilistic theory of causation, which analyses causal relations in terms of a three place statistical betweenness relation. Unlike most discussions of this theory, I hold that the statistical relation should be taken as a sufficient, but not as necessary, condition for causal betweenness. (...) With this difference in interpretation, Reichenbach's theory is shown to be immune to all of the criticisms which have been raised against it in the last.. (shrink)

The Facts of Causation grapples with one of philosophy's most enduring issues. Causation is central to all of our lives. What we see and hear causes us to believe certain facts about the world. We need that information to know how to act and how to cause the effects we desire. D. H. Mellor, a leading scholar in the philosophy of science and metaphysics, offers a comprehensive theory of causation. Many questions about causation remain unsettled. In science, the indeterminism of (...) modern physics and genetics have made such questions considerably harder for philosophers to answer. While progress has been made, a complete account of the nature and cosequences of causation is long overdue. This major study provides that account. (shrink)

We provide a formally rigorous framework for integrating singular causation, as understood by Nuel Belnap's theory of causae causantes, and objective single case probabilities. The central notion is that of a causal probability space whose sample space consists of causal alternatives. Such a probability space is generally not isomorphic to a product space. We give a causally motivated statement of the Markov condition and an analysis of the concept of screening-off. Causal dependencies and probabilities 1.1 Background: causation in branching space-times (...) 1.2 What are probabilities defined for? Basic transitions 2.1 Basics of basic transitions 2.2 Sets of basic transitions Causal probability theory 3.1 Some simple cases 3.2 General causal probabilities 3.3 Application: probability of suprema of a chain. (shrink)

Counter factual theories of Causation have had problems with cases of probabilistic causation and preemption. I put forward a counterfactual theory that seems to deal with these problematic cases and also has the virtue of providing an account of the alleged asymmetry between hasteners and delayers: the former usually being counted as causes, the latter not. I go on to consider a new type of problem case that has not received so much attention in the literature, those I dub catalysts (...) and anti-catalysts, and show how my account needs to be adjusted to deliver the right verdicts in these cases. The net result is a particular conception of a cause that I try to spell out in the closing section of the paper. In that section, I also briefly discuss causal asymmetry and the purpose behind providing a counterfactual theory of causation. (shrink)

A probabilistic and counterfactual theory of causality is developed within the framework of branching time. The theory combines ideas developed by James Fetzer, Donald Nute, Patrick Suppes, Ming Xu, John Pollock, David Lewis and Mellor among others.

Probabilistic accounts of causality have long had trouble with ‘spurious’ evidential correlations. Such correlations are also central to the case for causal decision theory—the argument that evidential decision theory is inadequate to cope with certain sorts of decision problem. However, there are now several strong defences of the evidential theory. Here I present what I regard as the best defence, and apply it to the probabilistic approach to causality. I argue that provided a probabilistic theory appeals to the notions of (...) agency and effective strategy, it can avoid the problem of spurious causes. I show that such an appeal has other advantages; and argue that it is not illegitimate, even for a causal realist. (shrink)

In a recent article, Paul Noordhof (1999) has put forward an intriguing account of causation intended to work under the assumption of indeterminism. I am going to present four problems for the account, three which challenge the necessity of the conditions he specifies, and one which challenges their joint-sufficiency.

Phil Dowe and Paul Noordhof Cause and Chance: Causation in an Indeterministic World London: Routledge, 2004, $35.00 ISBN: 0415408482, paperback.

The investigation of probabilistic causality has been plagued by a variety of misconceptions and misunderstandings. One has been the thought that the aim of the probabilistic account of causality is the reduction of causal claims to probabilistic claims. Nancy Cartwright (1979) has clearly rebutted that idea. Another ill-conceived idea continues to haunt the debate, namely the idea that contextual unanimity can do the work of objective homogeneity. It cannot. We argue that only objective homogeneity in combination with a causal interpretation (...) of Bayesian networks can provide the desired criterion of probabilistic causality. (shrink)