This survey presents some of the main principles involved in discovering causal relations. They belong to a large array of possible assumptions and conditions about causal relations, whose various combinations limit the possibilities of acquiring causal knowledge in different ways. How much and in what detail the causal structure can be discovered from what kinds of data depends on the particular set of assumptions one is able to make. The assumptions considered here provide a starting point to explore further the (...) foundations of causal discovery procedures, and how they can be improved. (shrink)

This paper argues for a particular account of luck by comparing two distinct versions of the modal account of luck that have been provided by Duncan Pritchard (2005, 2014). More specifically, it argues that there are three respects in which Pritchard’s earlier modal account of luck is preferable to his later account: it accounts better for the fact that luck comes in degrees, it includes a significance condition, and it better acknowledges the subjective nature of luck. The paper then discusses (...) two consequences of the points it makes for epistemology: an alleged pragmatic encroachment, and a particular view on the relation between knowledge, luck, and justification. (shrink)

Abstract: According to Laurence BonJour, the problem of induction can be solved by recognizing the a priori necessity that inductive conclusions constitute the best explanations of inductive premises. I defend an interpretation of the key probability claims BonJour makes about inductive premises and show that they are not susceptible to many of the objections that have been lodged against them. I then argue that these purportedly necessary probability claims nevertheless remain deeply problematic and that, as a result, BonJour's proposal fails (...) to provide a satisfactory resolution of the problem of induction. (shrink)

Cohen and Meskin 2006 recently offered a counterfactual theory of information to replace the standard probabilistic theory of information. They claim that the counterfactual theory fares better than the standard account on three grounds: first, it provides a better framework for explaining information flow properties; second, it requires a less expensive ontology; and third, because it does not refer to doxastic states of the information-receiving organism, it provides an objective basis. In this paper, I show that none of these is (...) really an advantage. Moreover, the counterfactual theory fails to satisfy one of the basic properties of information flow, namely the Conjunction principle. Thus, I conclude, there is no reason to give up the standard probabilistic theory for the counterfactual theory of information. (shrink)