James Woodward

This paper examines a recent attempt by Evan Jobe to account for the asymmetric character of many scientific explanations. It is argued that a purported counterexample to Jobe's account, from Clark Glymour, is inconclusive, but that the account faces independent objections. It is also suggested, contrary to Jobe, that the explanatory relation is not always asymmetric. Sometimes a singular sentence C can figure in a DN derivation of another singular sentence E and E can also figure in a DN derivation of C. Yet while we are inclined to regard the first derivation as an explanation of E, we are not inclined to regard the second derivation as an explanation of C. As Sylvain Bromberger pointed out in a now classic article (1966), one can explain the period of a pendulum by reference to its length and yet, although one can give a DN derivation of the length of a pendulum by reference to its period, this derivation does not seem to represent an explanation. Evan Jobe has recently offered an interesting account of such explanatory asymmetries and Clark Glymour has in turn proposed a counterexample which seems to show that Jobe's account is defective. The aim of this paper is two-fold. I shall attempt to show that (a) Glymour's proposed counterexample can be rejected on the grounds that it violates an independently plausible restriction on the role that equalities may play in DN explanation, and that (b) although Glymour's counterexample can be avoided in this way, Jobe's account is defective in several other respects

This paper attempts to elucidate three characteristics of causal relationships that are important in biological contexts. Stability has to do with whether a causal relationship continues to hold under changes in background conditions. Proportionality has to do with whether changes in the state of the cause “line up” in the right way with changes in the state of the effect and with whether the cause and effect are characterized in a way that contains irrelevant detail. Specificity is connected both to David Lewis’ notion of “influence” and also with the extent to which a causal relation approximates to the ideal of one cause–one effect. Interrelations among these notions and their possible biological significance are also discussed.

This paper explores some issues about the choice of variables for causal representation and explanation. Depending on which variables a researcher employs, many causal inference procedures and many treatments of causation will reach different conclusions about which causal relationships are present in some system of interest. The assumption of this paper is that some choices of variables are superior to other choices for the purpose of causal analysis. A number of possible criteria for variable choice are described and defended within a broadly interventionist approach to causation

We argue that Koch’s postulates are best understood within an interventionist account of causation, in the sense described in Woodward. We show how this treatment helps to resolve interpretive puzzles associated with Koch’s work and how it clarifies the different roles the postulates play in providing useful, yet not universal criteria for disease causation. Our paper is an effort at rational reconstruction; we attempt to show how Koch’s postulates and reasoning make sense and are normatively justified within an interventionist framework and more difficult to understand within alternative frameworks for thinking about causation.

This paper is an assessment of an attempt, by James Greeno, to measure the explanatory power of statistical theories by means of the notion of transmitted information (It). It is argued that It has certain features that are inappropriate in a measure of explanatory power. In particular, given a statistical theory T with explanans variables St and explanandum variables Mj, it is argued that no plausible measure of explanatory power should depend on the probability P(Si) of occurrence of initial conditions in the systems to which T applies or the magnitudes of the conditional probabilities P(Mj/Si), in the manner in which Ir does

This article defends the use of interventionist counterfactuals to elucidate causal and explanatory claims against criticisms advanced by James Bogen and Peter Machamer. Against Bogen, I argue that counterfactual claims concerning what would happen under interventions are meaningful and have determinate truth values, even in a deterministic world. I also argue, against both Machamer and Bogen, that we need to appeal to counterfactuals to capture the notions like causal relevance and causal mechanism. Contrary to what both authors suppose, counterfactuals are not "unscientific" - a substantial tradition within statistics and the causal modelling literature makes heavy use of them.

My focus in this essay is on those singular causal explanations which purport to explain the occurrence of some particular event by means of a claim of the following general sort The occurrence of event caused the occurrence of event.Examples include sentences like The short circuit caused the fire’ and The impact of the hammer caused the shattering of the glass,’ Many philosophers hold that there is a sharp distinction to be drawn between singular causal explanations and those sentences which simply report causal connections. While singular causal sentences are said to typically relate events, and to be extensional, singular causal explanations are held to relate items which are sentential or fact-like in structure and to be non-extensional.1 Singular causal explanations are held on this view to always be explanations of why events or other objects belong to certain kinds or possess certain properties.

It is noted that Popper separates the creation of concepts, conjectures, hypotheses and theories—the context of invention—from the testing thereof—the context of justification—arguing that only the latter is susceptible of rigorous logical analysis. Efforts on the part of others to shift or eradicate the demarcation established by this distinction are discussed and the relationship of these considerations to the claims of “strong artificial intelligence” is pointed out. It is argued that the mode of education of scientists, as well as reports of celebrated scientists, support Popper's judgement in this matter. An historical episode from Faraday's later career is used to illustrate the historiographical strength of Lakatos' “methodology of research programs.”

This paper describes an alternative to the common view that explanation in the special sciences involves subsumption under laws. According to this alternative, whether or not a generalization can be used to explain has to do with whether it is invariant rather than with whether it is lawful. A generalization is invariant if it is stable or robust in the sense that it would continue to hold under a relevant if it is stable or robust in the sense that it would continue to hold under a relevant class of changes. Unlike lawfulness, invariance comes in degrees and has other features that are well suited to capture the characteristics of explanatory generalizations in the special sciences. For example, a generalization can be invariant even if it has exceptions or holds only over a limited spatio-temporal interval. The notion of invariance can be used to resolve a number of dilemmas that arise in standard treatments of explanatory generalizations in the special sciences.

This paper explores the idea that laws express relationships between properties or universals as defended in Michael Tooley's recent book Causation: A Realist Approach. I suggest that the most plausible version of realism will take a different form than that advocated by Tooley. According to this alternative, laws are grounded in facts about the capacities and powers of particular systems, rather than facts about relations between universals. The notion of lawfulness is linked to the notion of invariance, rather than to the metaphysical notion of a necessary connection.

This paper explores some issues concerning the nature and structure of causal explanation in psychiatry and psychology from the point of view of the “interventionist” theory defended in my book, Making Things Happen. Among the issues is explored is the extent to which candidate causal explanations involving “upper level” or relatively coarse-grained or macroscopic variables such as mental/psychological states (e.g. highly self critical beliefs or low self esteem) or environmental factors (e.g. parental abuse) compete with explanations that instead appeal to underlying, “lower level” or more fine gained neural, genetic, or biochemical mechanisms.

In this paper I criticize the commonly accepted idea that the generalizations of the special sciences should be construed as ceteris paribus laws. This idea rests on mistaken assumptions about the role of laws in explanation and their relation to causal claims. Moreover, the major proposals in the literature for the analysis of ceteris paribus laws are, on their own terms, complete failures. I sketch a more adequate alternative account of the content of causal generalizations in the special sciences which I argue should replace the ceteris paribus conception.

This paper defends an interventionist treatment of mechanisms and contrasts this with Waskan (forthcoming). Interventionism embodies a difference-making conception of causation. I contrast such conceptions with geometrical/mechanical or “actualist” conceptions, associating Waskan’s proposals with the latter. It is argued that geometrical/mechanical conceptions of causation cannot replace difference-making conceptions in characterizing the behavior of mechanisms, but that some of the intuitions behind the geometrical/mechanical approach can be captured by thinking in terms of spatio-temporally organized difference-making information.

David-Hillel Ruben’s interesting and engaging book, Explaining Explanation, is in part an historical study, in part a commentary on the contemporary literature on explanation, and in part a presentation of Ruben’s own theory. The early chapters trace ideas about explanation Ruben finds in Plato, Aristotle, and Mill and connect these up with themes in the contemporary literature—for example, Plato’s criticisms of explanation by and of opposites are brought to bear on present-day issues concerning the structure of statistical explanation. This material struck me as fresh and illuminating, but since I’m no historian, I will concentrate mainly on Ruben’s own positive account, which is presented in the later chapters of EE.

Issues concerning scientific explanation have been a focus of philosophical attention from Pre- Socratic times through the modern period. However, recent discussion really begins with the development of the Deductive-Nomological (DN) model. This model has had many advocates (including Popper 1935, 1959, Braithwaite 1953, Gardiner, 1959, Nagel 1961) but unquestionably the most detailed and influential statement is due to Carl Hempel (Hempel 1942, 1965, and Hempel & Oppenheim 1948). These papers and the reaction to them have structured subsequent discussion concerning scientific explanation to an extraordinary degree. After some general remarks by way of background and orientation (Section 1), this entry describes the DN model and its extensions, and then turns to some well-known objections (Section 2). It next describes a variety of subsequent attempts to develop alternative models of explanation, including Wesley Salmon's Statistical Relevance (Section 3) and Causal Mechanical (Section 4) models and the Unificationist models due to Michael Friedman and Philip Kitcher (Section 5). Section 6 provides a summary and discusses directions for future work