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. (shrink)

It is often natural to compare two events by describing one as ‘more of a cause’ of some effect than the other. But what do such comparisons amount to, exactly? This paper aims to provide a guided tour of the recent literature on ‘degrees of causation’. Section 2 looks at what I call ‘dependence measures’, which arise from thinking of causes as difference‐makers. Section 3 looks at what I call ‘production measures’, which arise from thinking of causes as jointly sufficient (...) for their effects. Finally, section 4 examines the important question of whether there is any sense in which an agent is more responsible for an outcome in virtue of her action being more of a cause of it. I describe a puzzle that emerges from this question, and explore various strategies for resolving it. (shrink)

Which country, politician, or policy is more of a cause of the Covid-19 pandemic death toll? Which of the two factories causally contributed more to the pollution of the nearby river? A wide-ranging portion of our everyday thought and talk, and attitudes rely on a graded notion of causation. However, it is sometimes highlighted that on most contemporary accounts, causation is on-off. Some philosophers further question the legitimacy of talk of degrees of causation and suggest that we avoid it. Some (...) hold that the notion of degrees of causation is an illusion. In this paper, I’ll argue that causation does come in degrees. (shrink)

I argue for responsibility internalism. That is, moral responsibility (i.e., accountability, or being apt for praise or blame) depends only on factors internal to agents. Employing this view, I also argue that no one is responsible for what AI does but this isn’t morally problematic in a way that counts against developing or using AI. Responsibility is grounded in three potential conditions: the control (or freedom) condition, the epistemic (or awareness) condition, and the causal responsibility condition (or consequences). I argue (...) that causal responsibility is irrelevant for moral responsibility, and that the control condition and the epistemic condition depend only on factors internal to agents. Moreover, since what AI does is at best a consequence of our actions, and the consequences of our actions are irrelevant to our responsibility, no one is responsible for what AI does. That is, the so-called responsibility gap exits. However, this isn’t morally worrisome for developing or using AI. Firstly, I argue, current AI doesn’t generate a new kind of concern about responsibility that the older technologies don’t. Then, I argue that responsibility gap is not worrisome because neither responsibility gap, nor my argument for its existence, entails that no one can be justly punished, held accountable, or incurs duties in reparations when AI causes a harm. (shrink)

This PhD dissertation examines the conceptual and theoretical foundations of the most general and most widely used framework for understanding social evolution, W. D. Hamilton's theory of kin selection. While the core idea is intuitive enough (when organisms share genes, they sometimes have an evolutionary incentive to help one another), its apparent simplicity masks a host of conceptual subtleties, and the theory has proved a perennial source of controversy in evolutionary biology. To move towards a resolution of these controversies, we (...) need a careful and rigorous analysis of the philosophical foundations of the theory. My aim in this work is to provide such an analysis. I begin with an examination of the concepts behavioural ecologists employ to describe and classify types of social behaviour. I stress the need to distinguish concepts that are often conflated: for example, we need to distinguish simple cooperation from collaboration in collective tasks, behaviours from strategies, and control from manipulation and coercion. I proceed from here to the formal representation of kin selection via George R. Price’s covariance selection mathematics. I address a number of interpretative issues the Price formalism raises, including the vexed question of whether kin selection theory is ‘formally equivalent’ to multi-level selection theory. In the second half of the dissertation, I assess the uses and limits of Hamilton’s rule for the evolution of social behaviour; I provide a precise statement of the conditions under which the rival neighbour-modulated fitness and inclusive fitness approaches in contemporary kin selection theory are equivalent (and describe cases in which they are not); and I criticize recent formal attempts to establish the controversial claim that kin selection leads to organisms behaving as if maximizing their inclusive fitness. (shrink)

The statistical technique of analysis of variance is often used by biologists as a measure of causal factors’ relative strength or importance. I argue that it is a tool ill suited to this purpose, on several grounds. I suggest a superior alternative, and outline some 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.

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. (shrink)