Frederick Eberhardt

causal & statistical inference

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

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

Eberhardt, F., Hoyer, P.O., & Scheines, R. (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.

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

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

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

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

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

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

Eberhardt, F., Glymour, C. & Scheines, R. (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).

Eberhardt, F., Glymour, C. & Scheines, R. (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), AUAI Press: 178-184.

human learning

Danks, D. & Eberhardt, F. (in press). 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.

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

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

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

Hans Reichenbach

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

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

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

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

general machine learning / data mining

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

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

none of the above

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

drafts and submissions

Hyttinen, A., Eberhardt, F. and Hoyer, P. O. (submitted 10/2011). Learning linear cyclic causal models with latent variables.