Robert Northcott

Walsh on causes and evolution

Denis Walsh has written a striking new defense in this journal of the statisticalist (i.e., noncausalist) position regarding the forces of evolution. I defend the causalist view against his new objections. I argue that the heart of the issue lies in the nature of nonadditive causation. Detailed consideration of that turns out to defuse Walsh’s ‘description‐dependence’ critique of causalism. Nevertheless, the critique does suggest a basis for reconciliation between the two competing views.

It's just a feeling: why economic models do not explain

Julian Reiss correctly identified a trilemma about economic models: we cannot maintain that they are false, but nevertheless explain and that only true accounts explain. In this reply we give reasons to reject the second premise – that economic models explain. Intuitions to the contrary should be distrusted.

CAN ANOVA MEASURE CAUSAL STRENGTH

The statistical technique of analysis of variance is often used by biologists as a measure of the relative strength or importance of causal factors. I argue that it is a tool ill-suited to this purpose, on several grounds. I suggest a superior alternative and outline its implications. I finish with a diagnosis of the source of error—an unwitting inheritance of bad philosophy that now requires the remedy of better philosophy.

Verisimilitude: a causal approach

I present a new definition of verisimilitude, framed in terms of causes. Roughly speaking, according to it a scientific model is approximately true if it captures accurately the strengths of the causes present in any given situation. Against much of the literature, I argue that any satisfactory account of verisimilitude must inevitably restrict its judgments to context-specific models rather than general theories. We may still endorse—and only need—a relativized notion of scientific progress, understood now not as global advance but rather as the mastering of particular problems. This also sheds new light on longstanding difficulties surrounding language-dependence and models committed to false ontologies.

Weighted explanations in history

Weighted explanations , whereby some causes are deemed more important than others, are ubiquitous in historical studies. Drawing from influential recent work on causation, I develop a definition of causal-explanatory strength. This makes clear exactly which aspects of explanatory weighting are subjective and which objective. It also sheds new light on several traditional issues, showing for instance that: underlying causes need not be more important than proximate ones; several different causes can each be responsible for most of an effect; small causes need not be less important than big ones; and non-additive interactive effects between causes present no particular difficulty.

Pearson’s Wrong Turning: Against Statistical Measures of Causal Efficacy

Standard statistical measures of strength of association, although pioneered by Pearson deliberately to be acausal, nowadays are routinely used to measure causal efficacy. But their acausal origins have left them ill suited to this latter purpose. I distinguish between two different conceptions of causal efficacy, and argue that: (1) Both conceptions can be useful; (2) The statistical measures only attempt to capture the first of them; (3) They are not fully successful even at this; (4) An alternative definition based more squarely on causal thinking not only captures the second conception, but also can capture the first one better too.

Degree of explanation

Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures of degree of causation. Among other things, this reveals the precise role here of chance, as well as bearing on the relation between causal explanation and causation itself.

Genetic traits and causal explanation

I use a contrastive theory of causal explanation to analyze the notion of a genetic trait. The resulting definition is relational, an implication of which is that no trait is genetic always and everywhere. Rather, every trait may be either genetic or non-genetic, depending on explanatory context. I also outline some other advantages of connecting the debate to the wider causation literature, including how that yields us an account of the distinction between genetic traits and genetic dispositions.

The Scientific Study of Society, by Max Steuer. Kluwer Academic Publishers, 2003, xiii + 464 pages

Max Steuer’s readable book offers both an introduction to contemporary work in social science and also a defense of some general views about the nature of this kind of inquiry. Practicing social scientists will likely warm to its instinctive sympathy for their work. What of philosophers? Although both the author and Ken Binmore in the foreword are eager to deny that this book is an exercise in philosophy, its central claims – that a scientific study of society is possible and that its method is distinct from other ways of producing social knowledge – express meta-propositions about social science. What is distinct about Steuer’s approach is his conviction that these questions are best addressed not through abstract argument but rather by carefully examining what social scientists actually do. In this spirit, while chapters in the beginning and at the end of the book contain his general, or philosophical, discussion, at the heart of Steuer’s inquiry are six central chapters comprising long and painstaking reports of actual research. By the author’s own admission, the philosophical discussions at either end of the book are of a rather informal nature and do not seek to engage explicitly with the philosophical literature. Rather, the rhetorical strategy is one of argument by illustration. Does it succeed? The arguments presented in the early chapters are typical of a broadly naturalistic view of social science. Thus social science’s goals are taken to be similar in kind to those of natural science, and its relatively bad empirical record to be explained by a number of practical disadvantages it faces. One is that the phenomena studied by social science are subject to change at a much greater rate. Physical and biological phenomena also change, but many of their underlying principles are both quite stable and also directly relevant to explanation and intervention. In the social world, the underlying principles (for example, self-interested behavior in the