This paper addresses two examples due to Peter Achinstein purporting to show that the positive relevance view of evidence is too strong, that is, that evidence need not raise the probability of what it is evidence for. The first example can work only if it makes a false assumption. The second example fails because what Achinstein claims is evidence is redundant with information we already have. Without these examples Achinstein is left without motivation for his account of evidence, which uses (...) the concept of explanation in addition to that of probability. (shrink)

Germund Hesslow has argued recently [2] that a probabilistic theory of causality as advocated by Patrick Suppes [4] has two problems that a deterministic theory avoids. In this paper, I argue that Suppes' probabilistic causal calculus is free of each of these problems and, moreover, that several broader issues raised by Hesslow's discussion tend to support a probabilistic conception of causes.

A theory of the causal relate as aspects or property instances is developed. A supposed problem for transitivity is assessed and then resolved with aspects as the causal relata.

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 $\sim \text{C}\colon \ ch_{\text{C}}>ch_{\sim \text{C}}$. The book is as much about chance as it is about causation. I show that his way of distinguishing ch C from the traditional notion of conditional chance leaves him with a problem about the existence of ch Q when Q is false ; (...) 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. Other topics discussed may be gleaned from the headings below. (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)

I propose an amendment of Lewis's counterfactual analysis of causation, designed to overcome some difficulties concerning redundant causation.

Confirmation is commonly identified with positive relevance, E being said to confirm H if and only if E increases the probability of H. Today, analyses of this general kind are usually Bayesian ones that take the relevant probabilities to be subjective. I argue that these subjective Bayesian analyses are irremediably flawed. In their place I propose a relevance analysis that makes confirmation objective and which, I show, avoids the flaws of the subjective analyses. What I am proposing is in some (...) ways a return to Carnap's conception of confirmation, though there are also important differences between my analysis and his. My analysis includes new accounts of what evidence is and of the indexicality of confirmation claims. Finally, I defend my analysis against Achinstein's criticisms of the relevance concept of confirmation. (shrink)

"[This book] proposes new foundations for the Bayesian principle of rational action, and goes on to develop a new logic of desirability and probabtility."—Frederic Schick, _Journal of Philosophy_.

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)

Ever since David Hume, empiricists have barred powers and capacities from nature. In this book Cartwright argues that capacities are essential in our scientific world, and, contrary to empiricist orthodoxy, that they can meet sufficiently strict demands for testability. Econometrics is one discipline where probabilities are used to measure causal capacities, and the technology of modern physics provides several examples of testing capacities (such as lasers). Cartwright concludes by applying the lessons of the book about capacities and probabilities to the (...) explanation of the role of causality in quantum mechanics. (shrink)

La autora presenta algunas criticas generales al proyecto de reducir las leyes causales a probabilidades. Además, muestra que las leyes causales son imprescindibles para poder diferenciar las strategias efectivas de las que no lo son y da un criterio para considerar cuando podemos deducir causalidad a través de datos estadísticos.

Recently in this journal Sherrilyn Roush (2004) defends positive relevance as a necessary (albeit not a sufficient) condition for evidence by rejecting two of the counterexamples from my earlier (2001) work. In this reply I argue that Roush's critique is not successful.

What is required for something to be evidence for a hypothesis? In this fascinating, elegantly written work, distinguished philosopher of science Peter Achinstein explores this question, rejecting typical philosophical and statistical theories of evidence. He claims these theories are much too weak to give scientists what they want--a good reason to believe--and, in some cases, they furnish concepts that mistakenly make all evidential claims a priori. Achinstein introduces four concepts of evidence, defines three of them by reference to "potential" evidence, (...) and characterizes the latter using a novel epistemic interpretation of probability. The resulting theory is then applied to philosophical and historical issues. Solutions are provided to the "grue," "ravens," "lottery," and "old-evidence" paradoxes, and to a series of questions. These include whether explanations or predictions furnish more evidential weight, whether individual hypotheses or entire theoretical systems can receive evidential support, what counts as a scientific discovery, and what sort of evidence is required for it. The historical questions include whether Jean Perrin had non-circular evidence for the existence of molecules, what type of evidence J. J. Thomson offered for the existence of the electron, and whether, as is usually supposed, he really discovered the electron. Achinstein proposes answers in terms of the concepts of evidence introduced. As the premier book in the fabulous new series Oxford Studies in Philosophy of Science, this volume is essential for philosophers of science and historians of science, as well as for statisticians, scientists with philosophical interests, and anyone curious about scientific reasoning. (shrink)

I show that the connotations of causation - temporal, explanatory, predictive and means-end - are preserved in indeterministic causation only to the extent that effects have a greater chance of occurring in the circumstances if their causes do than if they don’t.