This paper describes the development of theories of scientific explanation since Hempel's earliest models in the 1940ies. It focuses on deductive and probabilistic whyexplanations and their main problems: lawlikeness, explanation-prediction asymmetries, causality, deductive and probabilistic relevance, maximal specifity and homogenity, the height of the probability value. For all of these topic the paper explains the most important approaches as well as their criticism, including the author's own accounts. Three main theses of this paper are: (1) Both deductive and probabilistic explanations (...) are important in science, not reducible to each other. (2) One must distinguish between (cause giving) explanations and (reason giving) justifications and predictions. (3) The adequacy of deductive as well as probabilistic explanations is relative to a pragmatically given background knowledge-which does not exclude, however, the possibility of purely semantic models. (shrink)

Recently, interest has surged in similarity-based epistemologies of possibility. However, it has been pointed out that the notion of ‘relevant similarity’ is not properly developed in this literature. In this paper, I look at the research done in the field of analogical reasoning, where we find that one of the most promising ways of capturing relevance in similarity reasoning is by relying on the predictive analogy similarity relation. This takes relevant similarity to be based on shared properties that have structural (...) relations to the property of interest. I argue that if we base our epistemology of possibility on similarity reasoning on the predictive analogy similarity relation, we require prior knowledge of the specifics of these structural relations. I discuss a number of possible responses to this on behalf of the similarity theorists given their methodological approach to the epistemology of modality more generally. They could either opt for making explicit the metaphysics underlying these structural relations, in which case they need to spell out how we can come to know these relations. Or they could opt for developing a theory that explains why we do not need to have explicit knowledge of these structural relations; for example by suggesting that we make use of epistemic shortcuts. (shrink)

Normic Laws and the Significance of Nonmonotonic Reasoning for Philosophy of Science. Normic laws have the form ‘if A then normally B’. They have been discovered in the explanation debate, but were considered as empirically vacuous (§1). I argue that the prototypical (or ideal) normality of normic laws implies statistical normality (§2), whence normic laws have empirical content. In §3–4 I explain why reasoning from normic laws is nonmonotonic, and why the understanding of the individual case is so important here. (...) After sketching some foundations of nonmonotonic reasoning as developed by AI-researchers (§5), Iargue that normic laws are also the best way to understand ceteris paribus laws (§6). §7 deals with the difference between physical and non-physical disciplines and §9 with the difference between normicity and approximation. In §8 it is shown how nonmonotonic reasoning provides a new understanding of the protection of theories against falsification by auxiliary hypotheses. §10, finally, gives a system- and evolution-theoretical explanation of the deeper reason for the omnipresence of normic laws in practice and science, and forthe connection between ideal and statistical normality. (shrink)

This paper examines a promising probabilistic theory of singular causation developed by David Lewis. I argue that Lewis' theory must be made more sophisticated to deal with certain counterexamples involving pre-emption. These counterexamples appear to show that in the usual case singular causation requires an unbroken causal process to link cause with effect. I propose a new probabilistic account of singular causation, within the framework developed by Lewis, which captures this intuition.

By a macro-level feature, I understand any feature that supervenes on, and is thus realized in, lower-level features. Recent discussions by Kim have suggested that such features cannot be causally relevant insofar as they are not classically reducible to lower-level features. This seems to render macro-level features causally irrelevant. I defend the causal relevance of some such features. Such features have been thought causally relevant in many examples that have underpinned philosophical work on causality. Additionally, in certain typical biological cases, (...) we conceive of causally relevant features at various compatible levels of analysis. When elaborated, these points make a strong prima facie case for macro-level causal relevance. However, we might abandon both the philosophical guideposts and the corresponding explanatory practice in the special sciences were we convinced that no reflective philosophical account could provide for the causal relevance there supposed. I show that such drastic measures are not necessary, for we can make sense of macro-level causal relevance by drawing on Paul Humphreys' recent work in ways suggested by the concrete examples considered here. (shrink)

An approach to inference to the best explanation integrating a Popperianconception of natural laws together with a modified Hempelian account of explanation, one the one hand, and Hacking's law of likelihood (in its nomicguise), on the other, which provides a robust abductivist model of sciencethat appears to overcome the obstacles that confront its inductivist,deductivist, and hypothetico-deductivist alternatives.This philosophy of scienceclarifies and illuminates some fundamental aspects of ontology and epistemology, especially concerning the relations between frequencies and propensities. Among the most important (...) elements of this conception is thecentral role of degrees of nomic expectability in explanation, prediction,and inference, for which this investigation provides a theoretical defense. (shrink)

Inconsistency of attitudes and behavior is due to the probabilistic connection between responses or actions and the (not directly observable) dispositions on which they depend. Latent variable models provide criteria for recognizing when attitude and behavior depend on the same disposition. Statistical tests of such models and techniques of parameter estimation are described. The viewpoint proposed here and illustrated with empirical examples contrasts with the prevalent reliance on correlational models and methods.

The Facts of Causation claims to be a “complete account of causation and its implications.” Mellor's concern is with singular causation; that is, where causes and effects are singular ). Singular causes and effects come in two sorts. Firstly, there are facts. Facts are actual states of affairs, and states of affairs correlate with whatever can be expressed in a sentence. For example, that Don falls and that Don dies are facts, if actual. In general terms, the causation of one (...) fact, E, by another, C, fits under the designation ‘E, because C’. (shrink)

To clarify and illuminate the place of probability in science Ellery Eells and James H. Fetzer have brought together some of the most distinguished philosophers ...

In this BA dissertation, I deploy examples of non-causal explanations of physical phenomena as evidence against the view that causal models of explanation can fully account for explanatory practices in science. I begin by discussing the problems faced by Hempel’s models and the causal models built to replace them. I then offer three everyday examples of non-causal explanation, citing sticks, pilots and apples. I suggest a general form for such explanations, under which they can be phrased as inductive-statistical arguments incorporating (...) plausible assumptions. I then show the applicability of this form to explanatory practices in thermal physics. I explore the possibility that population genetics provides a similar form of explanation, and offer a novel defence of the statistical interpretation of population genetics proposed by Matthen and Ariew (2002). I close with remarks concerning how, when faced with competing causal and non-causal explanations of the same phenomenon, we can perform an Inference to the Best Explanation. (shrink)

There are three main approaches to scientific explanation in the philosophical literature. The unification approach claims that science explains by fitting the particular facts and events within a general theoretical framework. The mechanistic approach claims that science explains by identifying mechanisms. According to the manipulationist approach an explanation ought to be such that it can be used to answer a “what-if-things-had-been-different question.” The article examines whether these three approaches are compatible or not in the case of the social sciences, and (...) it concludes by defending explanatory pluralism. (shrink)

It is argued in this paper that although much attention has been paid to causal chains and common causes within the literature on probabilistic causality, a primary virtue of that approach is its ability to deal with cases of multiple causation. In doing so some ways are indicated in which contemporary sine qua non analyses of causation are too narrow (and ways in which probabilistic causality is not) and an argument by Reichenbach designed to provide a basis for the asymmetry (...) of causation is refined. The importance of referring causal claims to an abstract model is also emphasized. (shrink)