All PDFs are subject to standard "fair use" conditions.
Please email (ddanks@cmu.edu) if there are issues/problems/questions about any papers.
Causal learning and reasoning (psychology)
Danks, D. (2007). Causal learning from observations and manipulations. In M. C. Lovett & P. Shah (Eds.), Thinking with data (pp. 359-388). New York: Lawrence Erlbaum Associates. [PDF (347k)]
Nichols, W, & Danks, D. (2007). Decision making using learned causal structures. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th annual meeting of the cognitive science society (pp. 1343-1348). Austin, TX: Cognitive Science Society. [PDF (111k)]
Danks, D., & Schwartz, S. (2006). Effects of causal strength on learning from biased sequences. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th annual meeting of the cognitive science society (pp. 1180-1185). Mahwah, NJ: Lawrence Erlbaum Associates. [PDF (191k)]
Danks, D., & Schwartz, S. (2005). Causal learning from biased sequences. In B. G. Bara, L. Barsalou, & M. Bucciarelli (Eds.), Proceedings of the 27th annual meeting of the cognitive science society (pp. 542-547). Mahwah, NJ: Lawrence Erlbaum Associates. [PDF (219k)]
Danks, D. (2004). Constraint-based human causal learning. In M. Lovett, C. Schunn, C. Lebiere, & P. Munro (Eds.), Proceedings of the 6th international conference on cognitive modeling (ICCM-2004) (pp. 342-343). Mahwah, NJ: Lawrence Erlbaum Associates. [PDF (47k)]
Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T. & Danks, D. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111 (1), 3-32. [PDF (951k)]
Danks, D. (2003). Equilibria of the Rescorla-Wagner model. Journal of Mathematical Psychology, 47, 109-121. [PDF (226k)]
Java applet implementing the equilibrium derivation procedure.
Danks, D., Griffiths, T. L., & Tenenbaum, J. B. (2003). Dynamical causal learning. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems 15 (pp. 67-74). Cambridge, MA: MIT Press. [PDF (148k)]
Kushnir, T., Gopnik, A., Schulz, L., & Danks, D. (2003). Inferring hidden causes. In R. Alterman & D. Kirsh (Eds.), Proceedings of the 25th annual meeting of the cognitive science society (pp. 699-703). Boston: Cognitive Science Society. [PDF (97k)]
Categorization and concepts (psychology)
Danks, D. (2007). Theory unification and graphical models in human categorization. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 173-189). Oxford: Oxford University Press. [PDF (167k)]
Zhu, H., & Danks, D. (2007). Task influences on category learning. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th annual meeting of the cognitive science society (pp. 1677-1682). Austin, TX: Cognitive Science Society. [PDF (109k)]
Danks, D. (2006). (Not) learning a complex (but learnable) category. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th annual meeting of the cognitive science society (pp. 1186-1191). Mahwah, NJ: Lawrence Erlbaum Associates. [PDF (208k)]
Danks, D. (2004). Psychological theories of categorization as probabilistic models. Technical report CMU-PHIL-157. July 15, 2004. [PDF (225k)]
Philosophy of psychology
Danks, D. (2008). Rational analyses, instrumentalism, and implementations. In N. Chater & M. Oaksford (Eds.), The probabilistic mind: Prospects for Bayesian cognitive science (pp. 59-75). Oxford: Oxford University Press. [PDF (166k)]
Danks, D., & Eberhardt, F. (in press). Conceptual problems in statistics, testing and experimentation. In J. Symons & F. Calvo (Eds.), Routledge companion to the philosophy of psychology. Routledge. [PDF (144k)]
Glymour, C., & Danks, D. (2007). Reasons as causes in Bayesian epistemology. Journal of Philosophy, 104(9), 464-474. [PDF (195k)]
Danks, D. (2005). The supposed competition between theories of human causal inference. Philosophical Psychology, 18 (2), 259-272. [PDF (85k)]
Other philosophy of science
Jantzen, B., & Danks, D. (in press). Biological codes and topological causation. Philosophy of Science. [PDF (209k)]
Danks, D. (2005). Scientific coherence and the fusion of experimental results. The British Journal for the Philosophy of Science, 56, 791-807. [PDF (116k)]
Machine learning
Danks, D. (in press). Learning. In K. Frankish & W. Ramsey (Eds.), Cambridge handbook to artificial intelligence. Cambridge: Cambridge University Press. [PDF (109k)]
Wimberly, F., Danks, D., Glymour, C., & Chu, T. (in press). Problems for structure learning: Aggregation and computational complexity. In S. Das, D. Caragea, W. H. Hsu, & S. M. Welch (Eds.), Computational methodologies in gene regulatory networks. Hershey, PA: IGI Global Publishing. [PDF forthcoming soon]
Danks, D. (2002). Learning the causal structure of overlapping variable sets. In S. Lange, K. Satoh, & C. H. Smith (Eds.), Discovery science: Proceedings of the 5th international conference (pp. 178-191). Berlin: Springer-Verlag. [PS (193k)]