Trumping the causal influence account of causation

Here is a simple counterexample to David Lewis’s causal influence account of causation, one that is especially illuminating due to its connection to what Lewis himself writes: it is a variant of his trumping example

Scalar implicature and contrastive explanation

I argue for a subsumption of any version of Grice’s first quantity maxim posited to underlie scalar implicature, by developing the idea of implicature recovery as a kind of explanatory inference, as e.g. in science. I take the applicable model to be contrastive explanation, while following van Fraassen’s analysis of explanation as an answer to a why-question. A scalar implicature is embedded in such an answer, one that meets two probabilistic constraints: the probability of the answer, and ‘favoring’. I argue that besides having application at large, outside of linguistic interpretation, these constraints largely account not only for implicatures based on strength order, logical and otherwise, but also for unordered cases. I thus suggest that Grice’s maxim and its descendants are expressions of general explanatory constraints, as they happen to be manifested in this particular explanatory task. I conclude by briefly discussing how I accordingly view Grice’s system outside of scalar implicature.     

Representing causation

The dynamics model, which is based on L. Talmy's (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1-3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed.     

Structural equations and causation

Structural equations have become increasingly popular in recent years as tools for understanding causation. But standard structural equations approaches to causation face deep problems. The most philosophically interesting of these consists in their failure to incorporate a distinction between default states of an object or system, and deviations therefrom. Exploring this problem, and how to fix it, helps to illuminate the central role this distinction plays in our causal thinking.

General causation

This paper outlines a general theory of efficient causation, a theory that deals in a unified way with traditional or deterministic, indeterministic, probabilistic, and other causal concepts. Theorists like Lewis, Salmon, and Suppes have attempted to broaden our causal perspective by reductively analysing causal notions in other terms. By contrast, the present theory rests in the first place on a non-reductive analysis of traditional causal concepts — into formal or structural components, on the one hand, and a physical or metaphysical component, on the other. The analyzans is then generalised. The theory also affords a more general propensity notion than is standard, one that helps solve major problems facing propensity interpretations of probability.     

Understanding causation

Synthese - In Part I of ‘Causality and Determination” (CD), Anscombe writes that (1) we understand causality through understanding specific causal expressions, (2) efficient causation...     

Conditional reasoning and causation

An experiment was conducted to investigate the relative contributions of syntactic form and content to conditional reasoning. The content domain chosen was that of causation. Conditional statements that described causal relationships (if mean value of cause, then mean value of effect) were embedded in simple arguments whose entailments are governed by the rules of truth-functional logic (i.e., modus ponens, modus tollens, denying the antecedent, and affirming the consequent). The causal statements differed in terms of the number of alternative causes and disabling conditions that characterized the causal relationship. (A disabling condition is an event that prevents an effect from occurring even though a relevant cause is present). Subjects were required to judge whether or not each argument's conclusion could be accepted. Judgements were found to vary systematically with the number of alternative causes and disabling conditions. Conclusions of arguments based on conditionals with few alternative causes or disabling conditions were found to be more acceptable than conclusions based on those with many.     

The demand for contrastive explanations

Philosophical Studies - A “contrastive explanation” explains not only why some event A occurred, but why A occurred as opposed to some alternative event B. Some philosophers argue that...     

Tooley on causation and probabilities

In this paper, I argue that there is an inconsistency between two presentist doctrines: that of ontological symmetry and asymmetry of fixity. The former refers to the presentist belief that the past and future are equally unreal. The latter refers to the A-Theoretic intuition that the past is closed or actual, and the future is open or potential. My position in this paper is that the presentist is unable to account for the temporal asymmetry that is so fundamentally a part of her theory. In Section I, I briefly outline a recent defence of presentism due to Craig, and argue that a flaw in this defence highlights the tension between the presentist's doctrines of ontological symmetry and asymmetry of fixity. In Section II, I undertake an investigation, on the presentist's behalf, in order to determine whether she is capable of reconciling these two doctrines. In the course of the investigation, I consider different asymmetries, other than that of ontology, which might be said fundamentally to constitute temporal asymmetry, and the asymmetry of fixity in particular. In Section III, I also consider whether the presentist is able to avail herself of some of the standard B-Theoretic accounts of the asymmetry of fixity, and argue that she cannot. Finally, I conclude that temporal asymmetry cannot be accounted for (or explained) other than through the postulation of an ontological asymmetry.     

Mental causation and explanatory exclusion

Kim argues that we can never have more than one complete and independent explanation for a single event. The existence of both mental and physical explanations for behavior would seem to violate this principle. We can avoid violating it only if we suppose that mental causal relationships supervene on physical causal relationships. I argue that although his solution is attractive in many respects, it will not do as it stands. I propose an alternate understanding of supervenient causation which preserves the advantages of Kim's account while avoiding the problems. My analysis involves appeal to counterfactuals. Any counterfactual analysis must confront the problem that mental states appear to be screened off from causal relevance by physical states. I argue that screening off is not a problem, because cases in which mental states appear to be screened off are cases in which background conditions are not held constant.     

Mental models and omissive causation

Some causal relations refer to causation by commission (e.g., “A gunshot causes death”), and others refer to causation by omission (e.g., “Not breathing causes death”). We describe a theory of the representation of omissive causation based on the assumption that people mentally simulate sets of possibilities—mental models—that represent causes, enabling conditions, and preventions (Goldvarg & Johnson-Laird, 2001). The theory holds that omissive causes, enabling conditions, and preventions each refer to distinct sets of possibilities. For any such causal relation, reasoners typically simulate one initial possibility, but they are able to consider alternative possibilities through deliberation. These alternative possibilities allow them to deliberate over finer-grained distinctions when reasoning about causes and effects. Hence, reasoners should be able to distinguish between omissive causes and omissive enabling conditions. Four experiments corroborated the predictions of the theory. We describe them and contrast the results with the predictions of alternative accounts of causal representation and inference.