Following in the fashion of Stephen Jay Gould and Peter Medawar, one of the world's leading scientists examines how "pure science" is in fact shaped and guided by social and political needs and assumptions.

In this paper we defend the view that the ordinary notions of cause and effect have a direct and essential connection with our ability to intervene in the world as agents.1 This is a well known but rather unpopular philosophical approach to causation, often called the manipulability theory. In the interests of brevity and accuracy, we prefer to call it the agency theory.2 Thus the central thesis of an agency account of causation is something like this: an event A is (...) a cause of a distinct event B just in case bringing about the occurrence of A would be an effective means by which a free agent could bring about the occurrence of B. In our view the unpopularity of the agency approach to causation may be traced to two factors. The first is a failure to appreciate certain distinctive advantages that this approach has over its various rivals. We have drawn attention to some of these advantages elsewhere, and we summarize below. However, the second and more important factor is the influence of a number of stock objections, objections that seem to have persuaded many philosophers that agency accounts face insuperable obstacles. In this paper we want to show that these objections have been vastly overrated. There are four main objections. (shrink)

Willard van Orman Quine once said that he had a preference for a desert ontology. This was in an earlier day when concerns with logical structure and ontological simplicity reigned supreme. Ontological genocide was practiced upon whole classes of upper-level or ‘derivative’ entities in the name of elegance, and we were secure in the belief that one strayed irremediably into the realm of conceptual confusion and possible error the further one got from ontic fundamentalism. In those days, one paid more (...) attention to generic worries about possible errors than to actual errors derived from distancing oneself too far from the nitty-gritty details of actual theory, actual inferences from actual data, the actual conditions under which we posited and detected entities, calibrated and ‘burned in’ instruments, identified and rejected artifacts, debugged programs and procedures, explained the mechanisms behind regularities, judged correlations to be spurious, and in general, to the real complexities and richness of actual scientific practice. (shrink)

My aim is twofold: first, to root out the metaphysical assumptions that generate the problem of mental causation and to show that they preclude its solution; second, to dissolve the problem of mental causation by motivating rejection of one of the metaphysical assumptions that give rise to it. There are three features of this metaphysical background picture that are important for our purposes. The first concerns the nature of reality: all reality depends on physical reality, where physical reality consists of (...) a network of events.1 The second concerns the nature of causation, and the third concerns the conception of behavior. I try to vindicate a robust idea of mental causation. (shrink)

1. Causal explanations depend on our interests, our purposes, and our prior knowledge. ⇒ No uniquely real causal explanation.

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Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal (...) mechanisms represented by causal networks can contribute to reasoning involving correlation and causation. The understanding of causation and causal mechanisms provides the basis for a presentation of the causal network instantiation model of medical explanation. (shrink)