Robert Northcott

The causal impacts of genes and environment on any one biological trait are inextricably entangled, and consequently it is widely accepted that it makes no sense in singleton cases to privilege either factor for particular credit. On the other hand, at a population level it may well be the case that one of the factors is responsible for more variation than the other. Standard methodological practice in biology uses the statistical technique of analysis of variance to measure this latter kind of causal efficacy. In this paper, I argue that: 1) analysis of variance is in fact badly suited to this role; and.

The 1994 US spectrum auction is now a paradigmatic case of the successful use of microeconomic theory for policy-making. We use a detailed analysis of it to review standard accounts in philosophy of science of how idealized models are connected to messy reality. We show that in order to understand what made the design of the spectrum auction successful, a new such account is required, and we present it here. Of especial interest is the light this sheds on the issue of progress in economics. In particular, it enables us to get clear on exactly what has been progressing, and on exactly what theory has – and has not – contributed to that. This in turn has important implications for just what it is about economic theory that we should value.

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.

Although a huge range of definitions has accumulated in the philosophy, biology and psychology literatures, no consensus has been reached on exactly what innateness amounts to. This has helped fuel an increasing skepticism, one that views the concept as anachronistic and actually harmful to science. Yet it remains central to many life sciences, and to several public policy issues too. So it is correspondingly urgent that its philosophical underpinnings be properly cleaned up. In this paper, I present a new approach that endorses a role in science for innateness while also accommodating many of the skeptical concerns. The key to squaring the circle is to import influential recent work on causal explanation. My thesis is that ascriptions of innateness are best seen as explanatory claims. The account that results has three main original features: 1) Innateness is a pragmatic, relational concept. Every trait may be either innate or non-innate, depending on explanatory context. 2) There is an important distinction between innate traits and innate dispositions. 3) Innateness is useful to science as a higher-level predicate that licenses interventions. It is thereby also clarified what ascriptions of innateness do not tell us.

Comparing different causes’ importance, and apportioning responsibility between them, requires making good sense of the notion of partial explanation, that is, of degree of explanation. How much is this subjective, how much objective? If the causes in question are probabilistic, how much is the outcome due to them and how much to simple chance? I formulate the notion of degree of causation, or effect size, relating it to influential recent work in the literature on causation. I examine to what extent mainstream social science methods--both quantitative and qualitative--succeed in establishing effect sizes so understood. The answer turns out to be, roughly: only to some extent. Next, the standard understanding of effect size, even though widespread, still has several underappreciated consequences. I detail some of those. Finally, I discuss the separate issue of explanandum-dependence, which is essential to assessing any cause’s explanatory importance and yet which has been comparatively neglected.

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