To evaluate Hume's thesis that causal claims are always empirical, I consider three kinds of causal statement: ?e1 caused e2 ?, ?e1 promoted e2 ?, and ?e1 would promote e2 ?. Restricting my attention to cases in which ?e1 occurred? and ?e2 occurred? are both empirical, I argue that Hume was right about the first two, but wrong about the third. Standard causal models of natural selection that have this third form are a priori mathematical truths. Some are obvious, others (...) less so. Empirical work on natural selection takes the form of defending causal claims of the first two types. I provide biological examples that illustrate differences among these three kinds of causal claim. (shrink)

ABSTRACT Agents in Frankfurt-style counterexamples only appear to be responsible insofar as they act willingly in the actual sequence, but would need to be manipulated against their will into forming the relevant intention in the alternative sequence. This difference appears ineliminable and unavoidably morally significant. ‘Neo-Frankfurtians’ concede that the sequences must be physically differentiated, but deny their moral differentiation. In contrast, I explore whether the alternatives could be physically undifferentiated, despite their moral difference. The reason there is an ineliminable moral (...) difference is because there is an ineliminable causal one: The agent’s choice must be caused by his own inclinations in one sequence, but by Black’s manipulation in the other. Whether this causal difference entails a physical one depends on whether we suppose causal relations conform to Humean supervenience. The denial of the latter requires us to either reject any broadly Humean view of causation, or else to suppose that we can individuate events or their qualities independently of the physical facts. If these are coherent possibilities, such examples may show that physically differentiated alternatives are not required for moral responsibility. This could support compatibilism, but only if such examples adequately parallel determinism, and I remain sceptical on this point. (shrink)

I argue that there are some causal relata for which it is indeterminate whether one caused the other. Positing indeterminacy in causation helps us defend contested principles in the logic of causation and makes possible new ways of thinking about the theoretical impact of symmetric causal overdetermination. I close by discussing amendments of current theories of causation that would help explain causal indeterminacy.

Philosophical theories of causation are commonly judged by their ability to correctly determine whether there is a causal relation present in intuitively clear example scenarios. If the theories survive this test, they are then used to answer big philosophical questions about causation. This Method of Examples is attractive because it seems to allow us to determine the quality of a theory of causation independently of answering the big philosophical questions; which is good, since it means that we can then non-circularly (...) use the theories judged to be best to answer those questions. However, the current article argues that this virtue of the Method of Examples is an illusion. In particular, I argue that the necessary step of judging whether a proposed analysis of causation is reductive can only be taken after many of the most vexing philosophical questions about causation have already been answered. It is then shown that a rejection of the methodological supremacy of the Method of Examples leads to a more pluralistic method of judging theories of causation, a pluralism that benefits non-standard approaches like interventionism and agency theories. (shrink)

I argue that there are some causal relata for which it is indeterminate whether one caused the other. Positing indeterminacy in causation helps us defend contested principles in the logic of causation and makes possible new ways of thinking about the theoretical impact of symmetric causal overdetermination. I close by discussing amendments of current theories of causation that would help explain causal indeterminacy.