Robert Northcott

I present a new case in which the Doomsday Argument runs afoul of epistemic intuition much more strongly than before. This leads to a dilemma: in the new case either DA is committed to unacceptable counterintuitiveness and belief in miracles, or else it is irrelevant. I then explore under what conditions DA can escape this dilemma. The discussion turns on several issues that have not been much emphasised in previous work on DA: a concern that I label trumping; the degree of uncertainty about relevant probability estimates; and the exact sequence in which we integrate DA and empirical concerns. I conclude that only given a particular configuration of these factors might DA still be of interest.

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. *Received December 2009; revised December 2009. †To contact the author, please write to: Department of Philosophy, 599 Lucas Hall, One University Boulevard, University of Missouri, St. Louis, MO 63121; e‐mail: [email protected].

Jonathan Schaffer (2004 ) proposes an ingenious amendment to David Lewis's semantics for counterfactuals. This amendment explicitly invokes the notion of causal independence, thus giving up Lewis's ambitions for a reductive counterfactual account of causation. But in return, it rescues Lewis's semantics from extant counterexamples. I present a new counterexample that defeats even Schaffer's amendment. Further, I argue that a better approach would be to follow the causal modelling literature and evaluate counterfactuals via an explicit postulated causal structure. This alternative approach easily resolves the new counterexample, as well as all the previous ones. Up to now, its perceived drawback relative to Lewis's scheme has been its non-reductiveness. But since the same drawback applies equally to Schaffer's amended scheme, this becomes no longer a point of comparative disadvantage.

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 more squarely based on causal thinking not only captures the second conception, it can also capture the first one better too.

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.