Online research in philosophy

Causal conditional reasoning means reasoning from a conditional statement that refers to causal content. We argue that data from causal conditional reasoning tasks tell us something not only about how people interpret conditionals, but also about how they interpret causal relations. In particular, three basic principles of people's causal understanding emerge from previous studies: the modal principle, the exhaustive principle, and the equivalence principle. Restricted to the four classic conditional inferences—Modus Ponens, Modus Tollens, Denial of the Antecedent, and Affirmation of the Consequent—causal conditional reasoning data are only partially able to support these principles. We present three experiments that use concrete and abstract causal scenarios and combine inference tasks with a new type of task in which people reformulate a given causal situation. The results provide evidence for the proposed representational principles. Implications for theories of the na ve understanding of causality are discussed.

What exactly is reasoning? Like many other philosophers, I shall endorse a broadly causal conception of reasoning. Reasoning is a causal process, in which one mental event (say, one’s accepting the conclusion of a certain argument) is caused by an antecedent mental event (say, one’s considering the premises of the argument). Just like causal accounts of action and causal accounts of perception, causal accounts of reasoning have to confront a version of what has come to be known as the problem of deviant causal chains. In this paper, I shall propose an account of the nature of reasoning, incorporating a solution to the specific version of the deviant causal chains problem that arises for accounts of reasoning. One striking feature of my solution is that it requires that certain normative facts are causally efficacious. It might be thought that this feature will make my account incompatible with any plausibly naturalistic approach to understanding the mind. I shall argue that this is not so: my account of the nature of reasoning is quite compatible with plausible versions of naturalism.

Reasoning about causation in fact is an essential element of attributing legal responsibility. Therefore, the automation of the attribution of legal responsibility requires a modelling effort aimed at the following: a thorough understanding of the relation between the legal concepts of responsibility and of causation in fact; a thorough understanding of the relation between causation in fact and the common sense concept of causation; and, finally, the specification of an ontology of the concepts that are minimally required for (automatic) common sense reasoning about causation. This article offers a worked-out example of the indicated analysis. Such example consists of: a definition of the legal concept of responsibility (in terms of liability and accountability); a definition of the legal concept of causation in fact (in terms of the initiation of physical processes by an agent and of the provision of reasons and/or opportunities to other agents); CausatiOnt, an AI-like ontology of the common sense (causal) concepts that are minimally needed for reasoning about the legal concept of causation in fact (in particular, the concepts of category, dimension, object, agent, process, event and act).

Non-reductive interventionist theories of causation and methodologies of causal reasoning embedded in that theoretical framework have become increasingly popular in recent years. This paper argues that one variant of an interventionist account of causation, viz. the one presented, for example, in Woodward (2003 ), is unsuited as a theoretical fundament of interventionist methodologies of causal reasoning, because it renders corresponding methodologies incapable of uncovering a causal structure in a finite number of steps. This finding runs counter to Woodward's own assessment and to other recent studies which presume that Woodward's version of interventionism is effectively applicable to uncover causal structures, e.g. Campbell (2007 ) or Shapiro and Sober (2007 ).

Hume, in "An Enquiry Concerning Human Understanding", holds (1) that all causal reasoning is based on experience and (2) that causal reasoning is based on nothing but experience. (1) does not imply (2), and Hume's good reasons for (1) are not good reasons for (2). This essay accepts (1) and argues against (2). A priori reasoning plays a role in causal inference. Familiar examples from Hume and from classroom examples of sudden disappearances and radical changes do not show otherwise. A priori causal reasoning is closely related to understanding causal mechanisms. One uncovers the intelligibility of a causal process by understanding its mechanism.

Background: Causal reasoning as a way to make a diagnosis seems convincing. Modern medicine depends on the search for causes of disease and it seems fair to assert that such knowledge is employed in diagnosis. Causal reasoning as it has been presented neglects to some extent the conception of multifactorial disease causes. Goal: The purpose of this paper is to analyze aspects of causation relevant for discussing causal reasoning in a diagnostic context.

Counterfactual theories of causation of the sort presented in Mackie, 1974, and Lewis, 1973 are a familiar part of the philosophical landscape. Such theories are typically advanced primarily as accounts of the metaphysics of causation. But they also raise empirical psychological issues concerning the processes and representations that underlie human causal reasoning. For example, do human subjects internally represent causal claims in terms of counterfactual judgments and when they engage in causal reasoning, does this involves reasoning about counterfactual claims? This paper explores several such issues from a broadly interventionist perspective.

Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal mechanisms represented by causal networks can contribute to reasoning involving correlation and causation. The understanding of causation and causal mechanisms provides the basis for a presentation of the causal network instantiation model of medical explanation.

Contemporary philosophical debates about causation are dominated by two approaches, which are often referred to as difference-making and causal process approaches to causation, respectively. I provide a characterization of the dialectic between these two approaches, on which that dialectic turns crucially on the question as to whether our common sense concept of causation involves a commitment to locality – i.e., to the claim that causal relations are always subject to spatial constraints. I then argue that we can extract from existing work on perceptual judgement (specifically work that invokes the notion of a simple theory of perception) materials for an argument in favour of a positive answer to that question, and that this work can also help to bring out a distinctive kind of role that a commitment to locality plays in our reasoning about causal relationships.

Although Elizabeth Anscombe’s work on causation is frequently cited and anthologized, her main arguments have been ignored or misunderstood as havingtheir basis in quantum mechanics or a particular theory of perception. I examine her main arguments and show that they not only work against the Humean causaltheories of her time, but also against contemporary attempts to analyze causation in terms of laws and causal properties. She shows that our ordinary usage does not connect causation with laws, and suggests that philosophers emphasize laws for mostly historical reasons. Moreover, she argues that the core of causation is derivativeness, which is as neglected now as when she wrote. Her focus on derivativeness indicates to us how we can both avoid the position that the causal “because” is truth-functional and yet still hold that causal statements are really explanatory.