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  1. Single Case Causes: What Is Evidence and Why.Nancy Cartwright - 2016 - In Hsiang-Ke Chao & Julian Reiss (eds.), Philosophy of Science in Practice: Nancy Cartwright and the nature of scientific reasoning. Cham: Springer International Publishing.
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  • Causes and Explanations: A Structural-Model Approach. Part I: Causes.Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):843-887.
    We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account. 1. Introduction2. Causal models: a review2.1Causal models2.2Syntax and semantics3. The definition of cause4. Examples5. A more refined definition6. DiscussionAAppendix: Some Technical IssuesA.1The active causal processA.2A closer look at AC2(b)A.3Causality with infinitely many variablesA.4Causality in nonrecursive (...)
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  • Structure induction in diagnostic causal reasoning.Björn Meder, Ralf Mayrhofer & Michael R. Waldmann - 2014 - Psychological Review 121 (3):277-301.
  • Nature's Capacities and Their Measurement.Tim Maudlin & Nancy Cartwright - 1993 - Journal of Philosophy 90 (11):599.
    This book on the philosophy of science argues for an empiricism, opposed to the tradition of David Hume, in which singular rather than general causal claims are primary; causal laws express facts about singular causes whereas the general causal claims of science are ascriptions of capacities or causal powers, capacities to make things happen. Taking science as measurement, Cartwright argues that capacities are necessary for science and that these can be measured, provided suitable conditions are met. There are case studies (...)
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  • Prevention, preemption, and the principle of sufficient reason.Christopher Hitchcock - 2007 - Philosophical Review 116 (4):495-532.
  • Prevention, Preemption, and the Principle of Sufficient Reason.Christopher Hitchcock - 2007 - Philosophical Review 116 (4):495-532.
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  • Causality: Models, Reasoning and Inference.Christopher Hitchcock & Judea Pearl - 2001 - Philosophical Review 110 (4):639.
    Judea Pearl has been at the forefront of research in the burgeoning field of causal modeling, and Causality is the culmination of his work over the last dozen or so years. For philosophers of science with a serious interest in causal modeling, Causality is simply mandatory reading. Chapter 2, in particular, addresses many of the issues familiar from works such as Causation, Prediction and Search by Peter Spirtes, Clark Glymour, and Richard Scheines. But philosophers with a more general interest in (...)
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  • Causes and explanations: A structural-model approach. Part I: Causes.Joseph Y. Halpern & Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):843-887.
    We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account.
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  • From covariation to causation: A causal power theory.Patricia W. Cheng - 1997 - Psychological Review 104 (2):367-405.
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  • Constraints and nonconstraints in causal learning: Reply to White (2005) and to Luhmann and Ahn (2005).Patricia W. Cheng & Laura R. Novick - 2005 - Psychological Review 112 (3):694-706.
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  • Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.
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  • Review: The Grand Leap; Reviewed Work: Causation, Prediction, and Search. [REVIEW]Peter Spirtes, Clark Glymour & Richard Scheines - 1996 - British Journal for the Philosophy of Science 47 (1):113-123.
  • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.J. Pearl, F. Bacchus, P. Spirtes, C. Glymour & R. Scheines - 1988 - Synthese 104 (1):161-176.
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