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Partial explanations in social science’

In Harold Kincaid (ed.), The Oxford Handbook of Philosophy of Social Science. Oxford University Press. pp. 130-153 (2012)

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  1. Harm and Causation.Robert Northcott - 2015 - Utilitas 27 (2):147-164.
    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 (...)
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  • Degree of Explanation.Robert Northcott - 2012 - Synthese 190 (15):3087-3105.
    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 (...)
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  • Conceived This Way: Innateness Defended.Northcott Robert - forthcoming - Philosophers Imprint.
    We propose a novel account of the distinction between innate and acquired biological traits: biological traits are innate to the degree that they are caused by factors intrinsic to the organism at the time of its origin; they are acquired to the degree that they are caused by factors extrinsic to the organism. This account borrows from recent work on causation in order to make rigorous the notion of quantitative contributions to traits by different factors in development. We avoid the (...)
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  • When Are Purely Predictive Models Best?Robert Northcott - unknown
    Can purely predictive models be useful in investigating causal systems? I argue ‘yes’. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to use theory to achieve explanation (...)
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