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  1. Hunting Causes and Using Them: Approaches in Philosophy and Economics.Nancy Cartwright (ed.) - 2007 - Cambridge University Press.
    Hunting Causes and Using Them argues that causation is not one thing, as commonly assumed, but many. There is a huge variety of causal relations, each with different characterizing features, different methods for discovery and different uses to which it can be put. In this collection of new and previously published essays, Nancy Cartwright provides a critical survey of philosophical and economic literature on causality, with a special focus on the currently fashionable Bayes-nets and invariance methods - and it exposes (...)
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  • Scientific Explanation and the Causal Structure of the World.Wesley C. Salmon - 1984 - Princeton University Press.
    The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes a robust (...)
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  • Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.
    Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.
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  • Dispositions.Stephen Mumford - 1998 - Clarendon Press.
    Stephen Mumford puts forward a new theory of dispositions, showing how central their role is in metaphysics and philosophy of science. Much of our understanding of the physical and psychological world is expressed in terms of dispositional properties--from the solubility of sugar to the belief that zebras have stripes. Mumford discusses what it means to say that something has a property of this kind, and how dispositions can possibly be real things in the world. His clear, straightforward, realist account reveals (...)
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  • Getting Causes From Powers.Stephen Mumford & Rani Lill Anjum - 2011 - Oxford University Press.
    Causation is everywhere in the world: it features in every science and technology. But how much do we understand it? Mumford and Anjum develop a new theory of causation based on an ontology of real powers or dispositions. They provide the first detailed outline of a thoroughly dispositional approach, and explore its surprising features.
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  • Causal Models: How People Think About the World and its Alternatives.Steven Sloman - 2005 - OUP.
    This book offers a discussion about how people think, talk, learn, and explain things in causal terms in terms of action and manipulation. Sloman also reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgement, categorization, inductive inference, language, and learning.
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  • Dispositions and Causal Powers.Kistler Max & Gnassounou Bruno (eds.) - 2007 - Ashgate.
    This collection of essays, by leading international researchers, examines the case for realism with respect to dispositions and causal powers in both ...
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  • Making Things Happen: A Theory of Causal Explanation.James Woodward - 2003 - Oxford University Press.
    Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.
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  • Independence, Invariance and the Causal Markov Condition.Daniel M. Hausman & James Woodward - 1999 - British Journal for the Philosophy of Science 50 (4):521-583.
    This essay explains what the Causal Markov Condition says and defends the condition from the many criticisms that have been launched against it. Although we are skeptical about some of the applications of the Causal Markov Condition, we argue that it is implicit in the view that causes can be used to manipulate their effects and that it cannot be surrendered without surrendering this view of causation.
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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.
  • From Covariation to Causation: A Causal Power Theory.Patricia W. Cheng - 1997 - Psychological Review 104 (2):367-405.
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  • Causation.David Lewis - 1973 - Journal of Philosophy 70 (17):556-567.
  • The Causal Asymmetry.Peter A. White - 2006 - Psychological Review 113 (1):132-147.
  • Modularity and the Causal Markov Condition: A Restatement.Daniel M. Hausman & James Woodward - 2004 - British Journal for the Philosophy of Science 55 (1):147-161.
    expose some gaps and difficulties in the argument for the causal Markov condition in our essay ‘Independence, Invariance and the Causal Markov Condition’ ([1999]), and we are grateful for the opportunity to reformulate our position. In particular, Cartwright disagrees vigorously with many of the theses we advance about the connection between causation and manipulation. Although we are not persuaded by some of her criticisms, we shall confine ourselves to showing how our central argument can be reconstructed and to casting doubt (...)
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  • The Psychological Causality Implicit in Language.Roger Brown & Deborah Fish - 1983 - Cognition 14 (3):237-273.
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  • Force Dynamics in Language and Cognition.Leonard Talmy - 1988 - Cognitive Science 12 (1):49-100.
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  • Inferring Causal Networks From Observations and Interventions.Mark Steyvers, Joshua B. Tenenbaum, Eric-Jan Wagenmakers & Ben Blum - 2003 - Cognitive Science 27 (3):453-489.
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  • The Misunderstood Limits of Folk Science: An Illusion of Explanatory Depth.Leonid Rozenblit & Frank Keil - 2002 - Cognitive Science 26 (5):521-562.
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  • Categorization as Causal Reasoning⋆.Bob Rehder - 2003 - Cognitive Science 27 (5):709-748.
    A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are (...)
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  • Naive Causality: A Mental Model Theory of Causal Meaning and Reasoning.Eugenia Goldvarg & P. N. Johnson-Laird - 2001 - Cognitive Science 25 (4):565-610.
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  • Judgments of Cause and Blame: The Effects of Intentionality and Foreseeability.David A. Lagnado & Shelley Channon - 2008 - Cognition 108 (3):754-770.
  • Spreading the Blame: The Allocation of Responsibility Amongst Multiple Agents.Tobias Gerstenberg & David A. Lagnado - 2010 - Cognition 115 (1):166-171.
  • Crime and Punishment: Distinguishing the Roles of Causal and Intentional Analyses in Moral Judgment.Fiery Cushman - 2008 - Cognition 108 (2):353-380.
    Recent research in moral psychology has attempted to characterize patterns of moral judgments of actions in terms of the causal and intentional properties of those actions. The present study directly compares the roles of consequence, causation, belief and desire in determining moral judgments. Judgments of the wrongness or permissibility of action were found to rely principally on the mental states of an agent, while judgments of blame and punishment are found to rely jointly on mental states and the causal connection (...)
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  • Bayesian Generic Priors for Causal Learning.Hongjing Lu, Alan L. Yuille, Mimi Liljeholm, Patricia W. Cheng & Keith J. Holyoak - 2008 - Psychological Review 115 (4):955-984.
  • Perception of Forces Exerted by Objects in Collision Events.Peter A. White - 2009 - Psychological Review 116 (3):580-601.
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  • An Enquiry Concerning Human Understanding.David Hume - 1955 - In Steven M. Cahn (ed.), Exploring Philosophy: An Introductory Anthology. Oxford University Press. pp. 112.
    David Hume's Enquiry concerning Human Understanding is the definitive statement of the greatest philosopher in the English language. His arguments in support of reasoning from experience, and against the "sophistry and illusion"of religiously inspired philosophical fantasies, caused controversy in the eighteenth century and are strikingly relevant today, when faith and science continue to clash. The Enquiry considers the origin and processes of human thought, reaching the stark conclusion that we can have no ultimate understanding of the physical world, or indeed (...)
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  • Covariation in Natural Causal Induction.Patricia W. Cheng & Laura R. Novick - 1992 - Psychological Review 99 (2):365-382.
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  • A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.Alison Gopnik, Clark Glymour, Laura Schulz, Tamar Kushnir & David Danks - 2004 - Psychological Review 111 (1):3-32.
    We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...)
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  • Representing Causation.Phillip Wolff - 2007 - Journal of Experimental Psychology: General 136 (1):82-111.
    The dynamics model, which is based on Talmy’s (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a (...)
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  • For Want of a Nail: How Absences Cause Events.Phillip Wolff, Aron K. Barbey & Matthew Hausknecht - 2010 - Journal of Experimental Psychology: General 139 (2):191-221.
  • Predictive and Diagnostic Learning Within Causal Models: Asymmetries in Cue Competition.Michael R. Waldmann & Keith J. Holyoak - 1992 - Journal of Experimental Psychology: General 121 (2):222-236.
  • Causal Models and the Acquisition of Category Structure.Michael R. Waldmann, Keith J. Holyoak & Angela Fratianne - 1995 - Journal of Experimental Psychology: General 124 (2):181.
  • Identification, Situational Constraint, and Social Cognition: Studies in the Attribution of Moral Responsibility.Robert L. Woolfolk, John M. Doris & John M. Darley - 2006 - Cognition 100 (2):283-301.
  • The Perception of Causality.A. Michotte, T. R. Miles & Elaine Miles - 1964 - British Journal for the Philosophy of Science 15 (59):254-259.
  • Theory-Based Causal Induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.
  • Models of Animal Learning and Their Relations to Human Learning.F. J. López & D. R. Shanks - 2008 - In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 589--611.
     
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