Linked bibliography for the SEP article "Causal Models" by Christopher Hitchcock
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- Balke, Alexander and Judea Pearl, 1994a, “Probabilistic Evaluation of Counterfactual Queries”, in Barbara Hayes-Roth and Richard E Korf (eds.), Proceedings of the Twelfth National Conference on Artificial Intelligence, Volume I, Menlo Park CA: AAAI Press, pp. 230–237. [Balke & Pearl 1994a available online] (Scholar)
- –––, 1994b, “Counterfactual Probabilities: Computational Methods, Bounds, and Applications”, in Ramon Lopez de Mantaras and David Poole (eds.), Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, pp. 46–54. [Balke & Pearl 1994b available online] (Scholar)
- Bareinboim, Elias, and Judea Pearl, 2013, “A General Algorithm for Deciding Transportability of Experimental Results”, Journal of Causal Inference, 1(1): 107–134. doi:10.1515/jci-2012-0004 (Scholar)
- –––, 2014, “Transportability from Multiple Environments with Limited Experiments: Completeness Results”, in Zoubin Ghahramani, Max Welling, Corinna Cortes, and Neil Lawrence and Kilian Weinberger (eds.), Advances of Neural Information Processing 27 (NIPS Proceedings), 280–288. [Bareinboim & Pearl 2014 available online] (Scholar)
- –––, 2015, “Causal Inference and the Data-Fusion Problem”, Proceedings of the National Academy of Sciences, 113(27): 7345–7352. doi:10.1073/pnas.1510507113 (Scholar)
- Beckers, Sander and Joost Vennekens, 2018, “A Principled Approach to Defining Actual Causation”, Synthese, 195(2): 835–862. doi:10.1007/s11229-016-1247-1 (Scholar)
- Beebee, Helen, Christopher Hitchcock, and Peter Menzies (eds.), 2009, The Oxford Handbook of Causation, Oxford: Oxford University Press. (Scholar)
- Blanchard, Thomas, and Jonathan Schaffer, 2017,“Cause without Default”, in Helen Beebee, Christopher Hitchcock, and Huw Price (eds.). Making a Difference, Oxford: Oxford University Press, pp. 175–214. (Scholar)
- Briggs, Rachael, 2012, “Interventionist Counterfactuals”, Philosophical Studies160(1): 139–166. doi:10.1007/s11098-012-9908-5 (Scholar)
- Cartwright, Nancy, 1993, “Marks and Probabilities: Two Ways to Find Causal Structure”, in Fritz Stadler (ed.), Scientific Philosophy: Origins and Development, Dordrecht: Kluwer, 113–119. doi:10.1007/978-94-017-2964-2_7 (Scholar)
- –––, 2007, Hunting Causes and Using Them, Cambridge: Cambridge University Press. doi:10.1017/cbo9780511618758 (Scholar)
- Chalupka, Krzysztof, Frederick Eberhardt, and Pietro Perona, 2017, “Causal Feature Learning: an Overview”, Behaviormetrika, 44(1): 137–167. doi:10.1007/s41237-016-0008-2 (Scholar)
- Cheng, Patricia, 1997, “From Covariation to Causation: A Causal Power Theory”, Psychological Review, 104(2): 367– 405. doi:10.1037/0033-295x.104.2.367 (Scholar)
- Claassen, Tom and Tom Heskes, 2012, “A Bayesian Approach to Constraint Based Causal Inference”, in Nando de Freitas and Kevin Murphy (eds.) Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, Corvallis, OR: AUAI Press, pp. 207–216. [Claassen & Heskes 2012 available online] (Scholar)
- Cooper, G. F. and Herskovits, E. 1992, “A Bayesian Method for the Induction of Probabilistic Networks from Data”, Machine Learning, 9(4): 309–347. doi:10.1007/bf00994110 (Scholar)
- Danks, David, and Sergey Plis, 2014, “Learning Causal Structure from Undersampled Time Series”, JMLR Workshop and Conference Proceedings (NIPS Workshop on Causality). [Danks & Plis 2014 available online] (Scholar)
- Dash, Denver and Marek Druzdzel, 2001, “Caveats For Causal Reasoning With Equilibrium Models”, in Salem Benferhat and Philippe Besnard (eds.) Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 6th European Conference, Proceedings. Lecture Notes in Computer Science 2143, Berlin and Heidelberg: Springer, pp.92–103. doi:10.1007/3-540-44652-4\_18 (Scholar)
- Dechter, Rina and Thomas Richardson (eds.), 2006, Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence, Corvallis, OR: AUAI Press. (Scholar)
- Dowe, Phil, 2000, Physical Causation, Cambridge: University of Cambridge Press. doi:10.1017/cbo9780511570650 (Scholar)
- Eberhardt, Frederick, 2009, “Introduction to the Epistemology of Causation”, Philosophy Compass, 4(6): 913–925. doi:10.1111/j.1747-9991.2009.00243.x (Scholar)
- –––, 2017, “Introduction to the Foundations of Causal Discovery”, International Journal of Data Science and Analytics, 3(2): 81–91. doi:10.1007/s41060-016-0038-6 (Scholar)
- Eberhardt, Frederick and Richard Scheines, 2007, “Interventions and Causal Inference”, Philosophy of Science, 74(5): 981–995. doi:10.1086/525638 (Scholar)
- Eells, Ellery, 1991, Probabilistic Causality, Cambridge: Cambridge University Press. doi:10.1017/cbo9780511570667 (Scholar)
- Eichler, Michael, 2012, “Causal Inference in Time Series Analysis”, in Carlo Berzuini, Philip Dawid, and Luisa Bernardinelli (eds.), Causality: Statistical Perspectives and Applications, Chichester, UK: Wiley, pp. 327–354. doi:10.1002/9781119945710.ch22 (Scholar)
- Fine, Kit, 2012, “Counterfactuals without Possible Worlds”, Journal of Philosophy, 109(3): 221–246. doi:10.5840/jphil201210938 (Scholar)
- Galles, David, and Judea Pearl, 1998, “An Axiomatic Characterization of Causal Counterfactuals”, Foundations of Science, 3(1): 151–182. doi:10.1023/a:1009602825894 (Scholar)
- Geiger, Dan and David Heckerman, 1994, “Learning Gaussian Networks”, Technical Report MSR-TR-94-10, Microsoft Research. (Scholar)
- Geiger, Dan and Judea Pearl, 1988, “On the Logic of Causal Models”, in Ross Shachter, Tod Levitt, Laveen Kanal, and John Lemmer (eds.), Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence, Corvallis, OR: AUAI Press, pp. 136–147. (Scholar)
- Gibbard, Alan, and William Harper, 1978, “Counterfactuals and Two Kinds of Expected Utility”, in Clifford Hooker, James Leach, and Edward McClennen (eds.), Foundations and Applications of Decision Theory, Dordrecht: Reidel, pp. 125–62. (Scholar)
- Glennan, Stuart, 2017, The New Mechanical Philosophy, Oxford: Oxford University Press. (Scholar)
- Glymour, Clark, 2009, “Causality and Statistics”, in Beebee, Hitchcock, and Menzies 2009: 498–522. (Scholar)
- Glymour, Clark and Gregory Cooper, 1999, Computation, Causation, and Discovery, Cambridge, MA: MIT Press. (Scholar)
- Glymour, Clark, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng, and Jiji Zhang, 2010, “Actual Causation: a Stone Soup Essay”, Synthese, 175(2): 169–192. doi:10.1007/s11229-009-9497-9 (Scholar)
- Glymour, Clark and Frank Wimberly, 2007, “Actual Causes and Thought Experiments”, in Joseph Campbell, Michael O’Rourke, and Harry Silverstein (eds.), Causation and Explanation, Cambridge, MA: MIT Press, pp. 43–68. (Scholar)
- Gong, Mingming, Kun Zhang, Bernhard Schölkopf, Dacheng Tao, and Philipp Geiger, 2015, “Discovering Temporal Causal Relations from Subsampled Data”, in Francis Bach and David Blei (eds.), Proceeding of the 32nd International Conference on Machine Learning, 37: 1898–1906. [Gong et al. 2015 available online] (Scholar)
- Gong, Mingming, Kun Zhang, Bernhard Schölkopf, Clark Glymour, and Dacheng Tao, 2017, “Causal Discovery from Temporally Aggregated Time Series”, in Gal Elidan and Kristian Kersting (eds.), Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, Corvallis, OR: AUAI Press. [Gong et al. 2017 available online] (Scholar)
- Greenland, Sander, and James Robins, 1988, “Conceptual Problems in the Definition and Interpretation of Attributable Fractions”, American Journal of Epidemiology, 128(6): 1185–1197. doi:10.1093/oxfordjournals.aje.a115073 (Scholar)
- Hall, Ned, 2007, “Structural Equations and Causation”, Philosophical Studies, 132(1): 109–136. doi:10.1007/s11098-006-9057-9 (Scholar)
- Halpern, Joseph Y., 2000, “Axiomatizing Causal Reasoning”, Journal of Artificial Intelligence Research, 12: 317–337. [Halpern 2000 available online] (Scholar)
- –––, 2008, “Defaults and Normality in Causal Structures”, in Gerhard Brewka and Jérôme Lang (eds.), Principles of Knowledge Representation and Reasoning: Proceedings of the Eleventh International Conference, Menlo Park, CA: AAAI Press, pp. 198–208. (Scholar)
- –––, 2016, Actual Causality, Cambridge, MA: MIT Press. (Scholar)
- Halpern, Joseph Y. and Christopher Hitchcock, 2015, “Graded Causation and Defaults”, British Journal for Philosophy of Science, 66(2): 413–57. doi:10.1093/bjps/axt050 (Scholar)
- Halpern, Joseph and Judea Pearl, 2001, “Causes and Explanations: A Structural-Model Approach. Part I: Causes”, in John Breese and Daphne Koller (eds.), Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, pp. 194–202 (Scholar)
- –––, 2005, “Causes and Explanations: A Structural-Model Approach. Part I: Causes”, British Journal for the Philosophy of Science, 56(4): 843–887. doi:10.1093/bjps/axi147 (Scholar)
- Hausman, Daniel M., 1999, “The Mathematical Theory of Causation”, British Journal for the Philosophy of Science, 50(1): 151–162. doi:10.1093/bjps/50.1.151 (Scholar)
- Hausman, Daniel M. and James Woodward, 1999, “Independence, Invariance, and the Causal Markov Condition”, British Journal for the Philosophy of Science, 50(4): 521–583. doi:10.1093/bjps/50.4.521 (Scholar)
- –––, 2004, “Modularity and the Causal Markov Condition: a Restatement”, British Journal for the Philosophy of Science, 55(1): 147–161. doi:10.1093/bjps/55.1.147 (Scholar)
- Hitchcock, Christopher, 2001, “The Intransitivity of Causation Revealed in Equations and Graphs”, Journal of Philosophy, 98(6): 273–299. doi:10.2307/2678432 (Scholar)
- –––, 2007, “Prevention, Preemption, and the Principle of Sufficient Reason”, Philosophical Review, 116(4): 495–532. doi:10.1215/00318108-2007-012 (Scholar)
- –––, 2009, “Causal Models”, in Beebee, Hitchcock, and Menzies 2009: 299–314. (Scholar)
- –––, 2016, “Conditioning, Intervening, and Decision”, Synthese, 193(4): 1157–1176. doi:10.1007/s11229-015-0710-8 (Scholar)
- Hoyer, Patrik O., Dominik Janzing, Joris Mooij, Jonas Peters, and Bernhard Schölkopf, 2009, “Nonlinear Causal Discovery with Additive Noise Models”, Advances in Neural Information Processing Systems, 21: 689–696. [Hoyer et al. 2009 available online] (Scholar)
- Huang, Yimin and Marco Valtorta, 2006, “Pearl’s Calculus of Intervention Is Complete”, in Dechter and Richardson 2006: 217–224. [Huang & Valtorta 2006 available online] (Scholar)
- Hyttinen, Antti, Frederick Eberhardt, and Patrik O. Hoyer, 2013a, “Experiment Selection for Causal Discovery”, Journal of Machine Learning Research, 14: 3041–3071. [Hyttinen, Eberhardt, & Hoyer 2013a available online] (Scholar)
- Hyttinen, Antti, Frederick Eberhardt, and Matti Järvisalo, 2014, “Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming”, in Nevin Zhang and Jin Tian (eds.), Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, Corvallis, OR: AUAI Press, pp. 340–349. (Scholar)
- –––, 2015, “Do-calculus When the True Graph is Unknown”, in Marina Meila and Tom Heskes (eds.), Uncertainty in Artificial Intelligence: Proceedings of the Thirty-First Conference, Corvallis, OR: AUAI Press, pp. 395–404. (Scholar)
- Hyttinen, Antti, Patrik O. Hoyer, Frederick Eberhardt, and Matti Järvisalo, 2013b, “Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure”, in Nichols and Smyth 2013: 301–310. (Scholar)
- Hyttinen, Antti, Sergey Plis, Matti Järvisalo, Frederick Eberhardt, and David Danks, 2016, “Causal Discovery from Subsampled Time Series Data by Constraint Optimization”, in Alessandro Antonucci, Giorgio Corani, Cassio Polpo Campos (eds.) Proceedings of the Eighth International Conference on Probabilistic Graphical Models, pp. 216–227. (Scholar)
- Jeffrey, Richard, 1983, The Logic of Decision, Second Edition, Chicago: University of Chicago Press. (Scholar)
- Joyce, James M., 1999, The Foundations of Causal Decision Theory, Cambridge: Cambridge University Press. doi:10.1017/cbo9780511498497 (Scholar)
- Lewis, David, 1973a, “Causation”, Journal of Philosophy, 70(17): 556–567. doi:10.2307/2025310 (Scholar)
- –––, 1973b, Counterfactuals, Oxford: Blackwell. (Scholar)
- –––, 1979, “Counterfactual Dependence and Time’s Arrow”, Noûs, 13(4): 455–476. doi:10.2307/2215339 (Scholar)
- –––, 1981, “Causal Decision Theory”, Australasian Journal of Philosophy, 59(1): 5–30. doi:10.1080/00048408112340011 (Scholar)
- Machamer, Peter, Lindley Darden, and Carl Craver, 2000, “Thinking about Mechanisms”, Philosophy of Science, 67(1): 1–25. doi:10.1086/392759 (Scholar)
- Maier, Marc, Katerina Marazopoulou, David Arbour, and David Jensen, 2013, “A Sound and Complete Algorithm for Learning Causal Models from Relational Data”, in Nichols and Smyth 2013: 371–380. [Maier et al. 2013 available online] (Scholar)
- Maier, Marc, Brian Taylor, Hüseyin Oktay, and David Jensen, 2010, “Learning Causal Models of Relational Domains”, in Maria Fox and David Poole (eds.), Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, (Menlo Park CA: AAAI Press), pp. 531–538. [Maier et al. 2010 available online] (Scholar)
- Meek, Christopher, 1995, “Strong Completeness and Faithfulness in Bayesian Networks”, in Philippe Besnard and Steve Hanks (eds.) Proceedings of the Eleventh Conference Conference on Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann, pp. 411–418. (Scholar)
- Meek, Christopher and Clark Glymour, 1994, “Conditioning and Intervening”, British Journal for Philosophy of Science,, 45(4): 1001–1024. doi:10.1093/bjps/45.4.1001 (Scholar)
- Menzies, Peter, 2004, “Causal Models, Token Causation, and Processes”, Philosophy of Science, 71(5): 820–832. doi:10.1086/425057 (Scholar)
- Mooij, Joris, Dominik Janzing, and Bernhard Schölkopf, 2013, “From Ordinary Differential Equations to Structural Causal Models: the Deterministic Case”, in Nichols and Smyth 2013: 440–448. (Scholar)
- Neal, Radford M., 2000, “On Deducing Conditional Independence from d-separation in Causal Graphs with Feedback”, Journal of Artificial Intelligence Research, 12: 87–91. [Neal 2000 available online] (Scholar)
- Neapolitan, Richard, 2004, Learning Bayesian Networks, Upper Saddle River, NJ: Prentice Hall. (Scholar)
- Neapolitan, Richard and Xia Jiang, 2016, “The Bayesian Network Story”, in Alan Hájek and Christopher Hitchcock (eds.), The Oxford Handbook of Probability and Philosophy, Oxford: Oxford University Press, pp. 183–99. (Scholar)
- Neyman, Jerzy, 1923 [1990], “Sur les Applications de la Théorie des Probabilités aux Experiences Agricoles: Essai des Principes”) Roczniki Nauk Rolniczych, Tom, X: 1–51. Excerpts translated into English by D. M. Dabrowska and Terrence Speed, 1990, “On the Application of Probability Theory to Agricultural Experiments. Essay on Principles”, Statistical Science, 5(4): 465–80. doi:10.1214/ss/1177012031 (Scholar)
- Ann Nichols and Padhraic Smyth (eds), 2013, Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, Corvallis, OR: AUAI Press. (Scholar)
- Nozick, Robert, 1969, “Newcomb’s Problem and Two Principles of Choice”, in Nicholas Rescher (ed.), Essays in Honor of Carl G. Hempel, Dordrecht: Reidel, pp. 114–146. doi:10.1007/978-94-017-1466-2_7 (Scholar)
- Pearl, Judea, 1988, Probabilistic Reasoning in Intelligent Systems, San Francisco: Morgan Kaufmann. (Scholar)
- –––, 1995, “Causal Diagrams for Empirical Research”, Biometrika, 82(4): 669–688. doi:10.1093/biomet/82.4.669 (Scholar)
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- Pearl, Judea, Madelyn Glymour, and Nicholas P. Jewell, 2016, Causal Inference in Statistics: A Primer, Chichester, UK: Wiley. (Scholar)
- Pearl, Judea and Mackenzie, Dana, 2018, The Book of Why: The New Science of Cause and Effect., New York: Basic Books. (Scholar)
- Pearl, Judea and Verma, Thomas, 1991, “A Theory of Inferred Causation”, in James Allen, Richard Fiskes, and Erik Sandewall (eds.), Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, San Mateo, CA: Morgan Kaufmann, pp. 441–52. (Scholar)
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- Scheines, Richard, 1997, “An Introduction to Causal Inference” in V. McKim and S. Turner (eds.), Causality in Crisis?, Notre Dame: University of Notre Dame Press, pp. 185–199. (Scholar)
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