Robert Northcott

I propose an analysis of harm in terms of causation: harm is when a subject is caused to be worse off. The pay-off from this lies in the details. In particular, importing influential recent work from the causation literature yields a contrastive-counterfactual account. This enables us to incorporate harm's multiple senses into a unified scheme, and to provide that scheme with theoretical ballast. It also enables us to respond effectively to previous criticisms of counterfactual accounts, as well as to sharpen criticisms of rival views.

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.

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.

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.

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.

Election prediction by means of opinion polling is a rare empirical success story for social science. I examine the details of a prominent case, drawing two lessons of more general interest: Methodology over metaphysics. Traditional metaphysical criteria were not a useful guide to whether successful prediction would be possible; instead, the crucial thing was selecting an effective methodology. Which methodology? Success required sophisticated use of case-specific evidence from opinion polling. The pursuit of explanations via general theory or causal mechanisms, by contrast, turned out to be precisely the wrong path—contrary to much recent philosophy of social science