David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Synthese 97 (3):335 - 364 (1993)
I advance a new theory of causal relevance, according to which causal claims convey information about conditional probability functions. This theory is motivated by the problem of disjunctive factors, which haunts existing probabilistic theories of causation. After some introductory remarks, I present in Section 3 a sketch of Eells's (1991) probabilistic theory of causation, which provides the framework for much of the discussion. Section 4 explains how the problem of disjunctive factors arises within this framework. After rejecting three proposed solutions, I offer in Section 6 a new approach to causation that avoids the problem. Decision-theoretic considerations also support the new approach. Section 8 develops the consequences of the new theory for causal explanation. The resulting theory of causal explanation incorporates the new insights while respecting important work on scientific explanation by Salmon (1971), Railton (1981), and Humphreys (1989). My conclusions are enumerated in Section 9.
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Citations of this work BETA
James Woodward & Christopher Hitchcock (2003). Explanatory Generalizations, Part I: A Counterfactual Account. Noûs 37 (1):1–24.
Elliott Sober (2011). A Priori Causal Models of Natural Selection. Australasian Journal of Philosophy 89 (4):571 - 589.
Christopher Read Hitchcock (1996). The Role of Contrast in Causal and Explanatory Claims. Synthese 107 (3):395 - 419.
Jonathan Schaffer (2010). Contrastive Causation in the Law. Legal Theory 16 (4):259-297.
Pablo Razeto-Barry & Ramiro Frick (2011). Probabilistic Causation and the Explanatory Role of Natural Selection. Studies in History and Philosophy of Science Part C 42 (3):344-355.
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