Systems involving many interacting variables are at the heart of the natural and social sciences. Causal language is pervasive in the analysis of such systems, especially when insight into their behavior is translated into policy decisions. This is exemplified by economics, but to an increasing extent also by biology, due to the advent of sophisticated tools to identify the genetic basis of many diseases. It is argued here that a regularity notion of causality can only be meaningfully defined for systems (...) with linear interactions among their variables. For the vastly more important class of nonlinear systems, no such notion is likely to exist. This thesis is developed with examples of dynamical systems taken mostly from mathematical biology. It is discussed with particular reference to the problem of causal inference in complex genetic systems, systems for which often only statistical characterizations exist. (shrink)

This collection of John Mackie's papers on topics in epistemology, some of which have not previously been published, deal with such issues as: incorrigible empirical statements; rationalism and empiricism; the philosophy of John Anderson; self-refutation; Plato's theory of idea; ideological explanation; problems of intentionality; Popper's third world;; mind, brain, and causation; Newcomb's Paradox and the direction of causation; induction; causation in concept, knowledge, and reality; absolutism; Locke and representative perception; and anti-realisms.

What exactly is a genetic disease? For a phrase one hears on a daily basis, there has been surprisingly little analysis of the underlying concept. Medical doctors seem perfectly willing to admit that the etiology of disease is typically complex, with a great many factors interacting to bring about a given condition. On such a view, descriptions of diseases like cancer as geneticseem at best highly simplistic, and at worst philosophically indefensible. On the other hand, there is clearly some practical (...) value to be had by classifying diseases according to theirpredominant cause when this can be accomplished in a theoretically satisfactory manner. The question therefore becomes exactly how one should go about selecting a single causal factor among many to explain the presence of disease. When an attempt to defend such causal selection is made at all, the standard accounts offered (Koch's postulates, Hill's epidemiological criteria, manipulability) are all clearly inadequate. I propose, however, an epidemiological account of disease causation which walks the fine line between practical applicability and theoretical considerations of causal complexity and attempts to compromise between patient-centered and population-centered concepts of disease. The epidemiological account is the most basic framework consistent with our strongly held intuitions about the causal classification of disease, yet it avoids the difficulties encountered by its competitors. (shrink)

Background: Causal reasoning as a way to make a diagnosis seems convincing. Modern medicine depends on the search for causes of disease and it seems fair to assert that such knowledge is employed in diagnosis. Causal reasoning as it has been presented neglects to some extent the conception of multifactorial disease causes. Goal: The purpose of this paper is to analyze aspects of causation relevant for discussing causal reasoning in a diagnostic context.