Perhaps the key philosophical questions concerning causality are the following: +what are causal relationships? how can one discover causal relationships? how should one reason with causal relationships? This chapter will focus on the first two questions.
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[Bacon, 1620] Francis Bacon: ‘The New Organon’, Lisa Jardine and Michael Silverthorne (eds.), Cambridge: Cambridge University Press 2000.
[Benacerraf, 1973] Paul Benacerraf: ‘Mathematical truth’, in [Benacerraf and Putnam, 1983], pages 403-420.
[Benacerraf and Putnam, 1983] Paul Benacerraf and Hilary Putnam (eds.): ‘Philosophy of mathematics: selected readings’, Cambridge: Cambridge University Press, second edition.
[Cartwright, 1997] Nancy Cartwright: ‘What is a causal structure?’, in [McKim and Turner, 1997], pages 343-357.
[Cartwright, 1999] Nancy Cartwright: ‘Causality: independence and determinism’, in [Gammerman, 1999], pages 51-63.
[Cartwright, 2001] Nancy Cartwright: ‘What is wrong with Bayes nets?’, The Monist 84(2), pages 242-264.
[Cooper, 1999] Gregory F. Cooper: ‘An overview of the representation and discovery of causal relationships using Bayesian networks’, in [Glymour and Cooper, 1999], pages 3-62.
[Cooper, 2000] Gregory F. Cooper: ‘A Bayesian method for causal modeling and discovery under selection’, in Proceedings of the Conference on Uncertainty in Artificial Intelligence 2000, pages 98-106.
[Corfield and Williamson, 2001] David Corfield and Jon Williamson (eds.): ‘Foundations of Bayesianism’, Kluwer Applied Logic Series, Dordrecht: Kluwer Academic Publishers.
[Dai et al., 1997] Honghua Dai, Kevin Korb, Chris Wallace and Xindong Wu: ‘A study of causal discovery with weak links and small samples’, in Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97), Nagoya, Japan, August 23-29 1997.
[Dash and Druzdzel, 1999] Denver Dash and Marek Druzdzel: ‘A Fundamental Inconsistency Between Causal Discovery and Causal Reasoning’, in Proceedings of the Joint Workshop on Conditional Independence Structures and the Workshop on Causal Interpretation of Graphical Models, The Fields Institute for Research in Mathematical Sciences, Toronto, Canada.
[Dawid, 2001] A.P. Dawid: ‘Causal inference without counterfactuals’, in [Corfield and Williamson, 2001], pages 37-74.
[Dowe, 1993] Phil Dowe: ‘On the reduction of process causality to statistical relations’, British Journal for the Philosophy of Science 44, pages 325-327.
[Dowe, 1996] Phil Dowe: ‘Backwards causation and the direction of causal processes’, Mind 105, pages 227-248.
[Dowe, 1999] Phil Dowe: ‘The conserved quantity theory of causation and chance raising’, Philosophy of Science 66 (Proceedings), pages S486-S501.
[Dowe, 2000] Phil Dowe: ‘Causality and explanation: Review of Salmon’, British Journal for the Philosophy of Science 51, pages 165-174.
[Dowe, 2000b] Phil Dowe: ‘Physical causation’, Cambridge: Cambridge University Press.
[Earman, 1992] John Earman: ‘Bayes or bust?’, Cambridge, Massachusetts: M.I.T. Press.
[Freedman and Humphreys, 1999] David Freedman and Paul Humphreys: ‘Are there algorithms that discover causal structure?’, Synthese 121, pages 29-54.
[Gammerman, 1999] Alex Gammerman (ed.): ‘Causal models and intelligent data management’, Berlin: Springer.
[Glymour, 1997] Clark Glymour: ‘A review of recent work on the foundations of causal inference’, [McKim and Turner, 1997], pages 201-248.
[Glymour, 2001] Clark Glymour: ‘The Mind’s Arrows: Bayes nets and graphical causal models in psychology’, Cambridge, Massachusetts: The M.I.T. Press.
[Glymour and Cooper, 1999] Clark Glymour and Gregory F. Cooper (eds.): ‘Computation, causation, and discovery’, Cambridge, Massachusetts: The M.I.T. Press.
[Hagmayer and Waldmann, 2002] York Hagmayer and Michael R. Waldmann: ‘A constraint satisfaction model of causal learning and reasoning’, in Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society, Mahwah, NJ: Erlbaum.
[Hausman, 1999] Daniel M. Hausman: ‘The mathematical theory of causation’, review of [McKim and Turner, 1997], British Journal for the Philosophy of Science 50, pages 151-162.
[Hausman and Woodward, 1999] Daniel M. Hausman and James Woodward: ‘Independence, invariance and the causal Markov condition’, British Journal for the Philosophy of Science 50, pages 521-583.
[Heckerman et al., 1999] David Heckerman, Christopher Meek and Gregory Cooper: ‘A Bayesian approach to causal discovery’, in [Glymour and Cooper, 1999], pages 141-165.
[Hempel and Oppenheim, 1948] Carl G. Hempel and Paul Oppenheim: ‘Studies in the logic of explanation’, with Postscript in [Pitt, 1988], pages 9-50.
[Howson and Urbach, 1989] Colin Howson and Peter Urbach: ‘Scientific reasoning: the Bayesian approach’, Chicago: Open Court, Second edition, 1993.
[Humphreys, 1997] Paul Humphreys: ‘A critical appraisal of causal discovery algorithms’, in [McKim and Turner, 1997], pages 249-263.
[Humphreys and Freedman, 1996] Paul Humphreys and David Freedman: ‘The grand leap’, British Journal for the Philosophy of Science 47, pages 113-123.
[James, 1907] William James: ‘What pragmatism means’, in ‘Pragmatism: A new name for some old ways of thinking’, New York: Longman Green and Co, pages 17-32.
[Karimi and Hamilton, 2000] Kamran Karimi and Howard J. Hamilton: ‘Finding Temporal Relations: Causal Bayesian Networks versus C4.5’, Proceedings of the Twelfth International Symposium on Methodologies for Intelligent System (ISMIS’2000), Charlotte, NC, USA, October 2000.
[Karimi and Hamilton, 2001] Kamran Karimi and Howard J. Hamilton: ‘Learning causal rules’, Technical Report CS-2001-03, Department of Computer Science, University of Regina, Saskatchewan, Canada.
[Korb, 1999] Kevin B. Korb: ‘Probabilistic causal structure’, in H. Sankey (ed.): ‘Causation and laws of nature’, Dordrecht: Kluwer, pages 265-311.
[Korb and Nicholson, 2003] Kevin B. Korb and Ann E. Nicholson: ‘Bayesian artificial intelligence’, London: Chapman and Hall / CRC Press UK.
[Lad, 1999] Frank Lad: ‘Assessing the foundation for Bayesian networks: a challenge to the principles and the practice’, Soft Computing 3(3), pages 174-180.
[Lemmer, 1996] John F. Lemmer: ‘The causal Markov condition, fact or artifact?’, SIGART Bulletin 7(3), pages 3-16.
[Lewis, 1973] David K. Lewis: ‘Causation’, with postscripts in [Lewis, 1986], pages 159-213.
[Lewis, 1980] David K. Lewis: ‘A subjectivist’s guide to objective chance’, in [Lewis, 1986], pages 83-132.
[Lewis, 1986] David K. Lewis: ‘Philosophical papers volume II’, Oxford: Oxford University Press.
[Lewis, 1986b] David K. Lewis: ‘Causal explanation’, in [Lewis, 1986], pages 214-240.
[Lewis, 2000] David K. Lewis: ‘Causation as influence’, The Journal of Philosophy 97(4), pages 182-197.
[Mani and Cooper, 1999] Subramani Mani and Gregory F. Cooper: ‘A study in causal discovery from population-based infant birth and death records’, in Proceedings of the AMIA Annual Fall Symposium 1999, Philadelphia: Hanley and Belfus Publishers, pages 315-319.
[Mani and Cooper, 2000] Subramani Mani and Gregory F. Cooper: ‘Causal discovery from medical textual data’, in Proceedings of the AMIA annual fall symposium 2000, Philadelphia: Hanley and Belfus Publishers, pages 542-546.
[Mani and Cooper, 2001] Subramani Mani and Gregory F. Cooper: ‘Simulation study of three related causal data mining algorithms’, in Proceedings of the International Workshop on Artificial Intelligence and Statistics 2001, San Francisco: Morgan Kaufmann, pages 73-80.
[McKim and Turner, 1997] Vaughn R. McKim and Stephen Turner: ‘Causality in crisis? Statistical methods and the search for causal knowledge in the social sciences’, Notre Dame: University of Notre Dame Press.
[Menzies and Price, 1993] Peter Menzies and Huw Price: ‘Causation as a secondary quality’, British Journal for the Philosophy of Science 44, pages 187-203.
[Pearl, 1988] Judea Pearl: ‘Probabilistic reasoning in intelligent systems: networks of plausible inference’, San Mateo, California: Morgan Kaufmann.
[Pearl, 1999] Judea Pearl: ‘Graphs, structural models, and causality’, in [Glymour and Cooper, 1999], pages 95-138.
[Pearl, 2000] Judea Pearl: ‘Causality: models, reasoning, and inference’, Cambridge: Cambridge University Press.
[Peirce, 1877] Charles Sanders Peirce: ‘The fixation of belief’, Popular Science Monthly 12, pages 1-15.
[Pitt, 1988] Joseph C. Pitt (ed.): ‘Theories of explanation’, Oxford: Oxford University Press.
[Polya, 1945] George Polya: ‘How to solve it’, second edition, Penguin 1990.
[Polya, 1954] George Polya: ‘Induction and analogy in mathematics’, volume 1 of ‘Mathematics and plausible reasoning’, Princeton: Princeton University Press.
[Polya, 1954b] George Polya: ‘Patterns of plausible inference’, volume 2 of ‘Mathematics and plausible reasoning’, Princeton: Princeton University Press.
[Popper, 1934] Karl R. Popper: ‘The Logic of Scientific Discovery’, with new appendices of 1959, London: Routledge 1999.
[Price, 1991] Huw Price: ‘Agency and probabilistic causality’, British Journal for the Philosophy of Science 42, pages 157-176.
[Price, 1992] Huw Price: ‘Agency and causal asymmetry’, Mind 101, pages 501-520.
[Price, 1992b] Huw Price: ‘The direction of causation: Ramsey’s ultimate contingency’, Philosophy of Science Association 1992(2), pages 253-267.
[Price, 1996] Huw Price: ‘Time’s arrow and Archimedes’ point: new directions for the physics of time’, New York: Oxford University Press.
[Price, 2001] Huw Price: ‘Causation in the special sciences: the case for pragmatism’, in Domenico Costantini, Maria Carla Galavotti and Patrick Suppes (eds.): ‘Stochastic Causality’, Stanford, California: CSLI Publications, pages 103-120.
[Price, 2003] Huw Price: ‘Truth as convenient friction’, Journal of Philosophy 100, pages 167-190.
[Price, 2004] Huw Price: ‘Models and modals’, in Donald Gillies (ed.): ‘Laws and models in science’, London: King’s College Publications.
[Railton, 1978] Peter Railton: ‘A deductive-nomological model of probabilistic explanation’, in [Pitt, 1988], pages 119-135.
[Reichenbach, 1956] Hans Reichenbach: ‘The direction of time’, Berkeley and Los Angeles: University of California Press 1971.
[Russell, 1913] Bertrand Russell: ‘On the notion of cause’, Proceedings of the Aristotelian Society 13, pages 1-26.
[Salmon, 1980] Wesley C. Salmon: ‘Causality: production and propagation’, in [Sosa and Tooley, 1993], chapter 9.
[Salmon, 1980b] Wesley C. Salmon: ‘Probabilistic causality’, in [Salmon, 1998], pages 208-232.
[Salmon, 1984] Wesley C. Salmon: ‘Scientific explanation and the causal structure of the world’, Princeton: Princeton University Press.
[Salmon, 1997] Wesley C. Salmon: ‘Causality and explanation: a reply to two critiques’, Philosophy of Science 64(3), pages 461-477.
[Salmon, 1998] Wesley C. Salmon: ‘Causality and explanation’, Oxford: Oxford University Press.
[Scheines, 1997] Richard Scheines: ‘An introduction to causal inference’, in [McKim and Turner, 1997], pages 185-199.
[Sosa and Tooley, 1993] Ernest Sosa and Michael Tooley (eds.): ‘Causation’, Oxford: Oxford University Press.
[Spirtes et al., 1993] Peter Spirtes, Clark Glymour and Richard Scheines: ‘Causation, Prediction, and Search’, Cambridge, Massachusetts: The M.I.T. Press, second edition 2000.
[Stankovski et al., 2001] V. Stankovski, I. Bratko, J. Demsar and D. Smrke: ‘Induction of hypotheses concerning hip arthoplasty: a modified methodology for medical research’, Methods of Information in Medicine 40, pages 392-396.
[Suppes, 1970] Patrick Suppes: ‘A probabilistic theory of causality’, Amsterdam: North-Holland.
[Tenenbaum and Griffiths, 2001] Joshua B. Tenenbaum and Thomas L. Griffiths: ‘Structure learning in human causal induction’, in T. Leen, T. Dietterich, and V. Tresp (eds.): Advances in Neural Information Processing Systems 13, Cambridge, Massachusetts: The M.I.T. Press, pages 59-65.
[Tong and Koller, 2001] Simon Tong and Daphne Koller: ‘Active Learning for Structure in Bayesian Networks’, in B. Nebel (ed.): Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, San Francisco: Morgan Kaufmann, pages 863-869.
[Waldmann, 2001] Michael R. Waldmann: ‘Predictive versus diagnostic causal learning: Evidence from an overshadowing paradigm’, Psychonomic Bulletin and Review 8, pages 600-608.
[Waldmann and Martignon, 1998] Michael R. Waldmann and Laura Martignon: ‘A Bayesian network model of causal learning’, in M.A. Gernsbacher and S.J. Derry (eds.): ‘Proceedings of the Twentieth Annual Conference of the Cognitive Science Society’, Mahwah, New Jersey: Erlbaum, pages 1102-1107.
[Wallace and Korb, 1999] Chris S. Wallace and Kevin B. Korb: ‘Learning linear causal models by MML sampling’, in [Gammerman, 1999], pages 88-111.
[Wendelken and Shastri, 2000] Carter Wendelken and Lokendra Shastri: ‘Probabilistic Inference and Learning in a Connectionist Causal Network’, In Proceedings of the Second International Symposium on Neural Computation, Berlin, May 2000.
[Williamson, 2004] Jon Williamson: ‘Bayesian nets and causality: philosophical and computational foundations’, Oxford: Clarendon Press.
[Woodward, 1997] James Woodward: ‘Causal models, probabilities, and invariance’, in [McKim and Turner, 1997], pages 265-315.
[Yoo et al., 2002] Changwon Yoo, V. Thorsson and G. Cooper: ‘Discovery of Causal Relationships in a Gene-regulation Pathway from a Mixture of Experimental and Observational DNA Microarray Data’, in Proceedings of the Pacific Symposium on Biocomputing, New Jersey: World Scientific, pages 498-509.
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Williamson, J. (2007). Causality. In: Gabbay, D., Guenthner, F. (eds) Handbook of Philosophical Logic. Handbook of Philosophical Logic, vol 14. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6324-4_2
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