Compact representations of extended causal models

Cognitive Science

Volumn 37, Issue 6, 2013, Pages 986-1010

  Halpern, Joseph Y ^a   Hitchcock, Christopher ^b  

^a Department of Obstetrics Gynecology   (United States)

^b CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Bayesian network; Causality; Compact representation; Normal; Typical

Indexed keywords

ARTICLE; BAYES THEOREM; BAYESIAN NETWORK; COMPACT REPRESENTATION; EPIDEMIOLOGY; HUMAN; NORMAL; PSYCHOLOGICAL MODEL; TYPICAL;

BAYESIAN NETWORK; CAUSALITY; COMPACT REPRESENTATION; NORMAL; TYPICAL;

BAYES THEOREM; CAUSALITY; HUMANS; MODELS, PSYCHOLOGICAL;

EID: 84881550476     PISSN: 03640213     EISSN: None     Source Type: Journal    
DOI: 10.1111/cogs.12059     Document Type: Article

References (31)

1. Adams, E. (1975). The logic of conditionals. Dordrecht, The Netherlands: Reidel.
2. Briggs, R. (2012). Interventionist counterfactuals. Philosophical Studies 160, 139-166.
3. Dubois, D., & Prade, H. (1991). Possibilistic logic, preferential models, non-monotonicity and related issues. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (IJCAI ?91), pp. 419-424.
4. Friedman, N., & Halpern, J. Y. (1995). Plausibility measures: A user's guide. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI ?95), pp. 175-184.
5. Friedman, N., & Halpern, J. Y. (2001). Plausibility measures and default reasoning. Journal of the ACM 48(4), 648-685.
6. Galles, D., & Pearl, J. (1997). Axioms of causal relevance. Artificial Intelligence 97(1-2), 9-43.
7. Galles, D., & Pearl, J. (1998). An axiomatic characterization of causal counterfactuals. Foundation of Science 3(1), 151-182.
8. Geffner, H. (1992). High probabilities, model preference and default arguments. Mind and Machines 2, 51-70.
9. Goldszmidt, M., & Pearl, J. (1992). Rank-based systems: A simple approach to belief revision, belief update and reasoning about evidence and actions. In Principles of Knowledge Representation and Reasoning: Proceedings of the Third International Conference (KR ?92), pp. 661-672.
10. Hall, N. (2007). Structural equations and causation. Philosophical Studies 132, 109-136.
11. Halpern, J. Y. (2000). Axiomatizing causal reasoning. Journal of A.I. Research 12, 317-337.
12. Halpern, J. Y. (2001). Conditional plausibility measures and Bayesian networks. Journal of A.I. Research 14, 359-389.
13. Halpern, J. Y. (2003). Reasoning about uncertainty. Cambridge, MA: MIT Press.
14. Halpern, J. Y. (2008). Defaults and normality in causal structures. In Principles of Knowledge Representation and Reasoning: Proceedings of the Eleventh International Conference (KR ?08), pp. 198-208.
15. Halpern, J. Y., & Hitchcock, C. (2010). Actual causation and the art of modeling. In R. Dechter, H. Geffner, & J. Y. Halpern (Eds.), Causality, probability, and heuristics: A tribute to Judea Pearl, pp. 383-406. London: College Publications.
16. Halpern, J. Y., & Hitchcock, C. (2011). Graded causation and defaults. Unpublished manuscript. Available at http://www.cs.cornell.edu/home/halpern/papers/normality.pdf.
17. Halpern, J. Y., & Pearl, J. (2005). Causes and explanations: A structural-model approach. Part I: Causes. British Journal for Philosophy of Science 56(4), 843-887.
18. Hitchcock, C. (2007). Prevention, preemption, and the principle of sufficient reason. Philosophical Review 116, 495-532.
19. Huber, F. (2011). Structural equations and beyond. Unpublished manuscript.
20. Knobe, J., & Fraser, B. (2008). Causal judgment and moral judgment: Two experiments. In W. Sinnott-Armstrong (Ed.), Moral psychology, Volume 2: The cognitive science of morality (pp. 441-447). Cambridge, MA: MIT Press.
21. Kraus, S. et al. (1990). Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence 44, 167-207.
22. Lewis, D. K. (1973). Counterfactuals. Cambridge, MA: Harvard University Press.
23. Menzies, P. (2004). Causal models, token causation, and processes. Philosophy of Science 71, 820-832.
24. Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Francisco: Morgan Kaufmann.
25. Pearl, J. (1989). Probabilistic semantics for nonmonotonic reasoning: A survey. In Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning (KR ?89), 505-516. Reprinted in G. Shafer and J. Pearl (Eds.), Readings in uncertain reasoning, pp. 699-710. San Francisco: Morgan Kaufmann, 1990.
26. Pearl, J. (1995). Causal diagrams for empirical research. Biometrika 82(4), 669-710.
27. Pearl, J. (2000). Causality: models, reasoning, and inference. New York: Cambridge University Press.
28. Shoham, Y. (1987). A semantical approach to nonmonotonic logics. In Proceedings of the 2nd IEEE Symposium on Logic in Computer Science, pp. 275-279. Reprinted in M. L. Ginsberg (Ed.), Readings in nonmonotonic reasoning, pp. 227-250. San Francisco: Morgan Kaufman, 1987.
29. Spohn, W. (1988). Ordinal conditional functions: A dynamic theory of epistemic states. In W. Harper & B. Skyrms (Eds.), Causation in decision, belief change, and statistics, vol. 2 (pp. 105-134). Dordrecht, The Netherlands: Reidel.
30. Stalnaker, R. C. (1968). A theory of conditionals. In N. Rescher (Ed.), Studies in logical theory (pp. 98-112). Oxford, England: Blackwell.
31. Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford, England: Oxford University Press.