publications

Frederick Eberhardt

causal & statistical inference

A. Hyttinen, F. Eberhardt & M. Järvisalo (2014). Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI).

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R. E. Tillman & F. Eberhardt (forthcoming). Learning causal structure from multiple datasets with similar variable sets. . In Behaviormetrika.

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F. Eberhardt (2013). Direct Causes and the Trouble with Soft Interventions. In Erkenntnis.

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A. Hyttinen, F. Eberhardt & P. O. Hoyer (2013). Experiment Selection for Causal Discovery. In Journal of Machine Learning Research, 14:3041-3071.

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F. Eberhardt (2013). Experimental Indistinguishability of Causal Structures. In Philosophy of Science (contributed paper to PSA 2012).

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A. Hyttinen, P. O. Hoyer, F. Eberhardt & M. Järvisalo (2013). Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI).

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A. Hyttinen, F. Eberhardt & P. O. Hoyer (2012). Learning Linear Cyclic Causal Models with Latent Variables. In Journal of Machine Learning Research, 13(Nov):3387-3439.

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A. Hyttinen, F. Eberhardt & P. O. Hoyer (2012). Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables. In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI).

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A. Hyttinen, F. Eberhardt & P. O. Hoyer (2011). Noisy-OR models with Latent Confounding. In Proceedings of 27th Conference in Uncertainty in Artificial Intelligence (UAI).

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A. Hyttinen, F. Eberhardt & P. O. Hoyer (2010). Causal discovery for linear cyclic models with latent variables. In Proceedings of the 5th European Workshop on Probabilistic Graphical Models (PGM).

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F. Eberhardt, P. O. Hoyer & R. Scheines (2010). Combining Experiments to Discover Linear Cyclic Models with Latent Variables. In Journal of Machine Learning, Workshop and Conference Proceedings (AISTATS 2010), 9:185-192.

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F. Eberhardt (2010). Causal Discovery as a Game. In Journal of Machine Learning, Workshop and Conference Proceedings (NIPS 2008 causality workshop), 6:87-96.

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F. Eberhardt (2009). Introduction to the Epistemology of Causation. In The Philosophy Compass, 4(6):913-925.

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F. Eberhardt (2008). Almost Optimal Intervention Sets for Causal Discovery. In Proceedings of 24th Conference in Uncertainty in Artificial Intelligence (UAI), 161-168.

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F. Eberhardt (2008). A Sufficient Condition for Pooling Data. In Synthese, special issue, 163(3):433-442.

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F. Eberhardt (2007). Causation and Intervention (Ph.D. Thesis). Carnegie Mellon University.

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F. Eberhardt & R. Scheines (2007). Interventions and Causal Inference. In Philosophy of Science, 74:981-995.

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F. Eberhardt, C. Glymour & R. Scheines (2006), N-1 Experiments Suffice to Determine the Causal Relations Among N Variables. In D. Holmes and L. Jain (eds.) Innovations in Machine Learning, Theory and Applications Series: Studies in Fuzziness and Soft Computing, Vol. 194, Springer-Verlag. See also Technical Report CMU-PHIL-161 (2005).

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F. Eberhardt, C. Glymour & R. Scheines (2005). On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables. In F. Bacchus and T. Jaakkola (eds.), Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence (UAI), 178-184; (Note the error described in the linked abstract.).

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human learning

D. Danks & F. Eberhardt (2011). Keeping Bayesian models rational: The need for an account of algorithmic rationality [Commentary on Jones & Love: Bayesian Fundamentalism or Enlightenment?]. In Behavioral and Brain Sciences, 34(4):197.

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F. Eberhardt & D. Danks (2011) Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. In Minds & Machines, 21(3):389-410.

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D. Danks & F. Eberhardt (2009). Explaining Norms and Norms Explained [Commentary on precis of Bayesian Rationality by Oaksford & Chater]. In Behavioral and Brain Sciences, 32:86-87.

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D. Danks & F. Eberhardt (2009). Conceptual Problems in Statistics, Testing and Experimentation. In J. Symons & F. Calvo (eds.) Routledge Companion to the Philosophy of Psychology, Routledge.

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Hans Reichenbach

F. Eberhardt (2011). Reliability via Synthetic A Priori - Reichenbach's Doctoral Thesis on Probability. In Synthese, 181(1):125-136.

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F. Eberhardt & C. Glymour (2011). Hans Reichenbach's Probability Logic. In D. M. Gabbay, J. Woods & S. Hartmann (eds.), Handbook of the History of Logic, Vol. 10, Elsevier.

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C. Glymour & F. Eberhardt (2008, updated 2012). Hans Reichenbach. Entry in Stanford Encyclopaedia of Philosophy.

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F. Eberhardt & C. Glymour (2008). The Concept of Probability in the Mathematical Representation of Reality. Translation of Hans Reichenbach's doctoral thesis, Open Court.

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general machine learning / data mining

B. Bryan, F. Eberhardt & C. Faloutsos (2008). Compact Similarity Joins. In Proceedings of 24th International Conference on Data Engineering (ICDE), IEEE: 346-355.

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S. Autexier, F. Eberhardt, D. Hutter, M. Kohlhase & R. Angelhache (2003). Distributed Knowledge Management and Version Control. In Information Society Technologies Programme, Report n. D5.a.

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none of the above

C. Glymour, D. Danks, F. Eberhardt, B. Glymour, J. Ramsey, R. Scheines, P. Spirtes, C. Teng & J. Zhang (2010). Actual Causation: A Stone Soup Essay. In Synthese, 175(2):169-192.

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Picture: tilework in Samarkand, Uzbekistan, 2009