Christopher Hitchcock

This chapter connects two themes in the work of Peter Menzies: (1) the agency theory of causation; and (2) the analysis of actual causation in terms of structural equation models together with considerations of normality. According to the latter type of analysis, actual causation involves certain kinds of _path-specific effects_. What is the practical benefit of knowing about such effects? The chapter argues that such knowledge is not necessary for one-shot decisions, but is crucial for plans that involve multiple steps. Such plans require that we know how our interventions will work in conjunction with future interventions that are feasible, expected, and desirable. This explains both the focus on path-specific effects, and the sensitivity of actual causation to considerations of normality.

Although there are problems with efforts to analyze causation in terms of counterfactuals, mainly stemming from pre-emption, it is clear that causation and counterfactuals are closely related. While philosophers have primarily been interested in questions concerning the semantics of counterfactuals—which counterfactuals are true, and what are the logical relations among counterfactuals and other claims—psychologists have been interested in the question of which counterfactual possibilities we actually entertain. Psychologists have enumerated a number of factors that render certain counterfactual possibilities readily ‘available’. I argue that we can expect these factors to exert a related influence on causal judgements, and connect this thesis with a number of discussions from the philosophical literature on causation.

Making a Difference presents fifteen original essays on causation and counterfactuals by philosophers and political theorists. Collectively, they represent the state of the art on these topics. The essays in this volume are inspired by the work of the late Australian philosopher Peter Menzies (1953–2015), who himself made a very great difference to our contemporary understanding of these matters. Topics covered include: the semantics of counterfactuals, agency theories of causation, the context-sensitivity of causal claims, structural equation models, mechanisms, mental causation, the causal exclusion argument, and free will. Contributors: Helen Beebee, Thomas Blanchard, David Braddon-Mitchell, Rachael Briggs, Nancy Cartwright, Christopher Hitchcock, Christian List, Cei Maslen, Peter Menzies, Daniel Nolan, Philip Pettit, Huw Price, Jonathan Schaffer, Brad Weslake, James Woodward.

__Contemporary Debates in Philosophy of Science_ contains sixteen original essays by leading authors in the philosophy of science, each one defending the affirmative or negative answer to one of eight specific questions, including: Are there laws of social science? Are causes physically connected to their effects? Is the mind a system of modules shaped by natural selection?_ Brings together fresh debates on eight of the most controversial issues in the philosophy of science. Questions addressed include: "Are there laws of social science?"; "Are causes physically connected to their effects?"; "Is the mind a system of modules shaped by natural selection?" Each question is treated by a pair of opposing essays written by eminent scholars, and especially commissioned for the volume. Lively debate format sharply defines the issues, and paves the way for further discussion. Will serve as an accessible introduction to the major topics in contemporary philosophy of science, whilst also capturing the imagination of professional philosophers.

__Contemporary Debates in Philosophy of Science_ contains sixteen original essays by leading authors in the philosophy of science, each one defending the affirmative or negative answer to one of eight specific questions, including: Are there laws of social science? Are causes physically connected to their effects? Is the mind a system of modules shaped by natural selection?_ Brings together fresh debates on eight of the most controversial issues in the philosophy of science. Questions addressed include: "Are there laws of social science?"; "Are causes physically connected to their effects?"; "Is the mind a system of modules shaped by natural selection?" Each question is treated by a pair of opposing essays written by eminent scholars, and especially commissioned for the volume. Lively debate format sharply defines the issues, and paves the way for further discussion. Will serve as an accessible introduction to the major topics in contemporary philosophy of science, whilst also capturing the imagination of professional philosophers.

Clark Glymour, together with his students Peter Spirtes and Richard Scheines, did pioneering work on graphical causal models (e.g. Spirtes et al., in Causation, prediction, and search, 2000). One of the central advances provided by these models is the ability to simply represent the effects of interventions. In an elegant paper (Meek and Glymour, in Br J Philos Sci 45: 1001–1021, 1994), 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.

Causal models have proven extremely useful in offering formal representations of causal relationships between a set of variables. Yet in many situations, there are non-causal relationships among variables. For example, we may want variables LDL, HDL, and TOT that represent the level of low-density lipoprotein cholesterol, the level of lipoprotein high-density lipoprotein cholesterol, and total cholesterol level, with the relation LDL+HDL=TOT. This cannot be done in standard causal models, because we can intervene simultaneously on all three variables. The goal of this paper is to extend standard causal models to allow for constraints on settings of variables. Although the extension is relatively straightforward, to make it useful we have to define a new intervention operation that disconnects a variable from a causal equation. We give examples showing the usefulness of this extension, and provide a sound and complete axiomatization for causal models with constraints.

The Common Cause Principle was introduced by HansReichenbach, in The Direction of Time, which was publishedposthumously in 1956. Suppose that two events A and Bare positively correlated: p(A∩B)>p(A)p(B)p(A∩B)>p(A)p(B)p(A\textbackslashcap B)>p(A)p(B). Suppose,moreover, that neither event is a cause of the other. Then,Reichenbach’s Common Cause Principle (RCCP) states that Aand B will have a common cause that renders them conditionallyindependent. Reichenbach incorporated his RCCP into a new probablistictheory of causation, and used it to describe a (purported)macrostatistical temporal asymmetry in analogy with the second law ofthermodynamics. The principle is significant because it posits aconnection between causal structure and probabilistic correlations,thus facilitating causal inference from observed correlations.However, RCCP has been controversial, and a number of counterexampleshave been proposed. RCCP is often seen as an antecedent of theCausal Markov Condition, which plays a central role in causalmodeling and causal inference. RCCP has also been taken to captureassumptions about the behavior of classical systems that appear to beviolated in quantum mechanics.

We introduce a family of rules for adjusting one's credences in response to learning the credences of others. These rules have a number of desirable features. 1. They yield the posterior credences that would result from updating by standard Bayesian conditionalization on one's peers' reported credences if one's likelihood function takes a particular simple form. 2. In the simplest form, they are symmetric among the agents in the group. 3. They map neatly onto the familiar Condorcet voting results. 4. They preserve shared agreement about independence in a wide range of cases. 5. They commute with conditionalization and with multiple peer updates. Importantly, these rules have a surprising property that we call synergy - peer testimony of credences can provide mutually supporting evidence raising an individual's credence higher than any peer's initial prior report. At first, this may seem to be a strike against them. We argue, however, that synergy is actually a desirable feature and the failure of other updating rules to yield synergy is a strike against them.

This chapter focuses on the connection between counterfactuals and causation, and on the use of causation in the analyses of other concepts, especially decision and dispositions. It briefly reviews two preliminary pieces of conceptual apparatus. The chapter divides Lewis's treatment of causation into three stages: The first is the theory presented in the 1973 paper "Causation.” The second includes the amendments included in postscripts to “Causation” in Philosophical Papers, Volume II, in 1986.The final stage is the theory from "Causation as Influence". Lewis introduced a number of subsidiary concepts: causal dependence among families of events, causal dependence among individual events, chancy causal dependence, quasi‐dependence, influence. The chapter presents a family tree of concepts, with causal dependence among families of variables at the trunk. These concepts are developed by Lewis in his attempts to analyze causation, and in his attempts to provide causal accounts of rational decision and dispositions.

Generally speaking, scientific explanation has been a topic of lively discussion in twentieth‐century philosophy of science; philosophers of science have endeavored to characterize rigorously a number of different types of explanation to be found in the various fields of scientific research. Given the indispensability of statistical concepts and techniques in virtually every branch of modern science, it is natural to ask whether some scientific explanations are essentially statistical or probabilistic in character. The answer would seem to be yes. For example, we can explain why atoms of carbon 14 have a 1/4 probability of surviving for 11,460 years because the half‐life of that species is 5,730 years. As we shall see, explanations of this type are not especially problematic. As another example, we might explain why a particular weed withered by citing the fact that it received a dose of a herbicide, even though we know that the herbicide is not invariably effective. This means that the withering is related probabilistically to the herbicide treatment but is not necessitated by it. Explanations of this kind, by contrast, lead to severe difficulties.

In Belief and the Will, van Fraassen employed a diachronic Dutch Book argument to support a counterintuitive principle called Reflection. There and subsequently van Fraassen has put forth Reflection as a linchpin for his views in epistemology and the philosophy of science, and for the voluntarism (first-person reports of subjective probability are undertakings of commitments) that he espouses as an alternative to descriptivism (first-person reports of subjective probability are merely self-descriptions). Christensen and others have attacked Reflection, taking it to have unpalatable consequences. We prescind from the question of the cogency of diachronic Dutch Book arguments, and focus on Reflection's proper interpretation. We argue that Reflection is not as counterintuitive as it appears — that once interpreted properly the status of the counterexamples given by Christensen and others is left open. We show also that descriptivism can make sense of Reflection, while voluntarism is not especially well suited to do so.