Christopher Hitchcock

There are many ways of attaching two objects together: for example, they can be connected, linked, tied or bound together; and the connection, link, tie or bind can be made of chain, rope, or cement. Every one of these binding methods has been used as a metaphor for causation. What is the real significance of these metaphors? They express a commitment to a certain way of thinking about causation, summarized in the following thesis: ‘In any concrete situation, there is an objective fact of the matter as to whether two events are in fact bound by the causal relation. It is the aim of philosophical inquiry to analyze this objective relation.’ Through a variety of examples, I hope to cast doubt on this seemingly innocuous thesis. The problem lies not with the word ‘objective’, but with the word ‘the’. The goal of a philosophical account of causation should not be to capture the causal relation, but rather to capture the many ways in which the events of the world can be bound together. 1 The metaphors 2 Unpacking the metaphors 3 Theories of causation 4 The two assassins 5 The birth control pills 6 The smoker-protector gene 7 The bicycle thief 8 Further examples 8.1 Indeterminism 8.2 Probability-lowering causes 8.3 Parts vs wholes 8.4 Symmetric overdetermination 8.5 Delayers 8.6 Causation by omission 8.7 Double prevention/disconnection 8.8 Preemptive prevention 8.9 Quantitative variables 9 Conclusion.

Carter and Leslie (1996) have argued, using Bayes's theorem, that our being alive now supports the hypothesis of an early 'Doomsday'. Unlike some critics (Eckhardt 1997), we accept their argument in part: given that we exist, our existence now indeed favors 'Doom sooner' over 'Doom later'. The very fact of our existence, however, favors 'Doom later'. In simple cases, a hypothetical approach to the problem of 'old evidence' shows that these two effects cancel out: our existence now yields no information about the coming of Doom. More complex cases suggest a move from countably additive to non-standard probability measures.

There is a tradition, tracing back to Kant, of recasting metaphysical questions as questions about the utility of a conceptual scheme, linguistic framework, or methodological rule for achieving some particular end. Following in this tradition, I propose a ‘means-ends metaphysics ’, in which one rigorously demonstrates the suitability of some conceptual framework for achieving a specified goal. I illustrate this approach using a debate about the nature of events. Specifically, the question is whether the time at which an event occurs is an essential property of that event. I argue that this question is naturally transformed into a question about the methodology of causal modeling. In this new framework, the question concerns what kind of variables to use to represent the effects of potential interventions on a system. This question has a demonstrably correct answer, which sheds new light on the original question

Clark Glymour, together with his students Peter Spirtes and Richard Scheines, did pioneering work on graphical causal models . One of the central advances provided by these models is the ability to simply represent the effects of interventions. In an elegant paper , Glymour and his student Christopher Meek applied these methods to problems in decision theory. One of the morals they drew was that causal decision theory should be understood in terms of interventions. I revisit their proposal, and extend the analysis by showing how graphical causal models might be used to address decision problems that arise in “exotic” situations, such as those involving crystal balls or time travelers.

Probability theory is a key tool of the physical, mathematical, and social sciences. It has also been playing an increasingly significant role in philosophy: in epistemology, philosophy of science, ethics, social philosophy, philosophy of religion, and elsewhere. This Handbook encapsulates and furthers the influence of philosophy on probability, and of probability on philosophy. Nearly forty articles summarise the state of play and present new insights in various areas of research at the intersection of these two fields. The articles will be of special interest to practitioners of probability who seek a greater understanding of its mathematical and conceptual foundations, and to philosophers who want to get up to speed on the cutting edge of research in this area. The volume begins with a primer on those parts of probability theory that we believe are most important for philosophers to know, and the rest is divided into seven main sections: History; Formalism; Alternatives to Standard Probability Theory; Interpretations and Interpretive Issues; Probabilistic Judgment and Its Applications; Applications of Probability: Science; and Applications of Probability: Philosophy.

Concerning any object of philosophical analysis, we can ask several questions, including the two posed in the title of this paper. Despite difficulties in formulating a precise criterion to distinguish causal processes from pseudoprocesses, and causal interactions from mere spatiotemporal intersections, I argue that Salmon answered the first of these questions with extraordinary clarity. The second question, by contrast, has received very little attention. I will present two problems: in the first, it seems that Salmon has provided exactly the conceptual resources needed to solve the problem; in the second, it is difficult to see how causal processes and interactions may be used to shed any light. In general, the way to carry Salmon's program forward will be to demonstrate that these resources can be made to do real philosophical work.

It is a commonplace that in philosophy, intuitions supply evidence for and against philosophical theories. Recent work in experimental philosophy has brought to bear the intuitions of philosophically naïve subjects in a number of different ways. One line of response to this work has been to claim that philosophers have expertise that privileges their intuitive judgments, and allows them to disregard the judgments of non-experts. This expertise is supposed to be analogous to the expertise of the mathematician or the physicist. This paper critically evaluates this appeal to philosophical expertise.

Larry Wright and others have advanced causal accounts of functional explanation, designed to alleviate fears about the legitimacy of such explanations. These analyses take functional explanations to describe second order causal relations. These second order relations are conceptually puzzling. I present an account of second order causation from within the framework of Eells' probabilistic theory of causation; the account makes use of the population-relativity of causation that is built into this theory.

We provide a solution to the well-known “Shooting-Room” paradox, developed by John Leslie in connection with his Doomsday Argument. In the “Shooting-Room” paradox, the death of an individual is contingent upon an event that has a 1/36 chance of occurring, yet the relative frequency of death in the relevant population is 0.9. There are two intuitively plausible arguments, one concluding that the appropriate subjective probability of death is 1/36, the other that this probability is 0.9. How are these two values to be reconciled? We show that only the first argument is valid for a standard, countably additive probability distribution. However, both lines of reasoning are legitimate if probabilities are non-standard. The subjective probability of death rises from 1/36 to 0.9 by conditionalizing on an event that is not measurable, or whose probability is zero. Thus we can sometimes meaningfully ascribe conditional probabilities even when the event conditionalized upon is not of positive finite (or even infinitesimal) measure.

an observation to formulate a theory, it is no surprise that the resulting theory accurately captures that observation. However, when the theory makes a novel prediction—when it predicts an observation that was not used in its formulation—this seems to provide more substantial confirmation of the theory. This paper presents a new approach to the vexed problem of understanding the epistemic difference between prediction and accommodation. In fact, there are several problems that need to be disentangled; in all of them, the key is the concept of overfitting. We float the hypothesis that accommodation is a defective methodology only when the methods used to accommodate the data fail to guard against the risk of overfitting. We connect our analysis with the proposals that other philosophers have made. We also discuss its bearing on the conflict between instrumentalism and scientific realism. Introduction Predictivisms—a taxonomy Observations Formulating the problem What might Annie be doing wrong? Solutions Observations explained Mayo on severe tests The miracle argument and scientific realism Concluding comments.

Much of the philosophical literature on causation has focused on the concept of actual causation, sometimes called token causation. In particular, it is this notion of actual causation that many philosophical theories of causation have attempted to capture.2 In this paper, we address the question: what purpose does this concept serve? As we shall see in the next section, one does not need this concept for purposes of prediction or rational deliberation. What then could the purpose be? We will argue that one can gain an important clue here by looking at the ways in which causal judgments are shaped by people‘s understanding of norms.