Online research in philosophy

Temporal analyses of causal directionality fail if causes needn't precede their effects. Certain well-known difficulties with alternative (non-temporal) analyses have, in recent accounts, been avoided by attending more carefully to the formal features of relations typically figuring in philosophical discussions of causation. I discuss here a representative of such accounts, offered by David Sanford, according to which a correct analysis of causal priority must issue from viewing the condition relation as nonsymmetrical. The theory is shown first to be an implicitly (...) counterfactual treatment at its base: this provides for an explicit reformulation of several key notions in the theory. An argument is then presented, independent of these modal considerations, for the conclusion that causal priority is possible only given certain implausible assumptions about the asymmetric character of causal laws which, I claim, are not met. The best objection to this argument is shown to fail on several counts, partly in light of the counterfactual results offered earlier. It is concluded that, if laws are symmetric, analyses of the sort discussed must look elsewhere for the source of causal priority; but if laws are not symmetric, resting causal priority so heavily on nomological asymmetry is no analysis at all. (shrink)

The invariance under interventions –account of causal explanation imposes a modularity constraint on causal systems: a local intervention on a part of the system should not change other causal relations in that system. This constraint has generated criticism against the account, since many ordinary causal systems seem to break this condition. This paper answers to this criticism by noting that explanatory models are always models of specific causal structures, not causal systems as a whole, and that models of causal structures (...) can have different modularity properties which determine what can and what cannot be explained with the model. (shrink)

I defend my attempt to explain causal priority by means of one-way causal conditionship by answering an argument by J. A. Cover about Charles'' law. Then I attempt to say what makes a philosophical analysis a counterfactual analysis, so I can understand Cover''s claim that my account is at its base a counterfactual one. Finally I examine Cover''s discussion of my contention that necessary for in the circumstances is nontransitive.

Decision making typically requires judgements about causal relations: we need to know both the causal e¤ects of our actions and the causal relevance of various environmental factors. Judgements about the nature and strength of causal relations often di¤er, even among experts. How to handle such diversity is the topic of this paper. First we consider the possibility of aggregating causal judgements via the aggregation of probabilistic ones. The broadly negative outcome of this investigation leads us to look at aggregating causal (...) judgements independently of probabilistic ones. We do so by transcribing causal claims into the judgement aggregation framework and applying some recent results in this …eld. Finally we look at the implications for probability aggregation when it is constrained by prior aggregation of causal judgements. (shrink)

According to the transitive dynamics model, people can construct causal structures by linking together configurations of force. The predictions of the model were tested in two experiments in which participants generated new causal relationships by chaining together two (Experiment 1) or three (Experiment 2) causal relations. The predictions of the transitive dynamics model were compared against those of Goldvarg and Johnson-Laird’s model theory (Goldvarg & Johnson- Laird, 2001). The transitive dynamics model consistently predicted the overall causal relationship drawn by participants (...) for both types of causal chains, and, when compared to the model theory, provided a better fit to the data. The results suggest that certain kinds of causal reasoning may depend on force dynamic—rather than on logical or purely statistical—representations. (shrink)

The literature on causation distinguishes between causal claims relating properties or types and causal claims relating individuals or tokens. Many authors maintain that corresponding to these two kinds of causal claims are two different kinds of causal relations. Whether to regard causal relations among variables as yet another variety of causation is also controversial. This essay maintains that causal relations obtain among tokens and that type causal claims are generalizations concerning causal relations among these tokens.

The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant (...) Human paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models: e.g. Rubin’s model, contingency tables, and multilevel analysis. It is also shown to be latent – yet fundamental – in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability. This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science. "Dr. Federica... more on http://springer.com/978-1-4020-8816-2.. (shrink)

In this paper it will be argued that causal laws describe the actions of causal powers. The process which results from such an action is one which belongs to a natural kind, the essence of which is that it is a display of this causal power. Therefore, if anything has a given causal power necessarily, it must be naturally disposed to act in the manner prescribed by the causal law describing the action of this causal power. In the formal expressions (...) of causal laws, the necessity operators occur within the scopes of the universal quantifiers. Hence the necessities must hold of each instance. The causal laws may thus be shown to be concerned with necessary connections between events or circumstances of precisely the sort required for a decent account of singular causation. (shrink)

A common line of argument for the impossibility of closed causal loops is that they would involve causal paradoxes. The usual reply is that such loops impose heavy consistency constraints on the nature of causal connections in them; constraints that are overlooked by the impossibility arguments. Hugh Mellor has maintained that arguments for the possibility of causal loops also overlook some constraints, which are related to the chances (single-case, objective probabilities) that causes give to their effects. And he argues that (...) a consideration of these constraints demonstrates that causal loops are impossible. I consider Mellor's argument and more generally the nature of chance in causal loops. I argue that Mellor's line of reasoning is unwarranted since it is based on untenable premisses about the relation between chances and long-run frequencies in causal loops. Yet, this line of reasoning may still be of interest to those who maintain that causes determine the chances of their effects; for it raises some unresolved questions about the nature of chance in causal loops. (shrink)

We further develop the mathematical theory of causal interventions, extending earlier results of Korb, Twardy, Handfield, & Oppy, (2005) and Spirtes, Glymour, Scheines (2000). Some of the skepticism surrounding causal discovery has concerned the fact that using only observational data can radically underdetermine the best explanatory causal model, with the true causal model appearing inferior to a simpler, faithful model (cf. Cartwright, (2001). Our results show that experimental data, together with some plausible assumptions, can reduce the space of viable explanatory (...) causal models to one. (shrink)