Homogeneity, selection, and the faithfulness condition

Minds and Machines

Volumn 16, Issue 3, 2006, Pages 303-317

  Steel, Daniel ^a  

^a MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Causal inference; Directed acyclic graphs; Faithfulness condition

Indexed keywords

GRAPH THEORY; MATHEMATICAL MODELS; STATISTICAL METHODS; THEOREM PROVING;

CAUSAL INFERENCE; DIRECTED ACYCLIC GRAPHS; FAITHFULNESS CONDITION;

DATA STRUCTURES;

EID: 33751196788     PISSN: 09246495     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11023-006-9032-4     Document Type: Article

References (24)

1. Billingsley, P. (1995). Probability and measure (3rd edn.). New York, NY: Wiley.
2. Cartwright, N. (1999a). The dappled word: a study of the boundaries of science. Cambridge: Cambridge University Press.
3. Cartwright, N. (1999b). Causal diversity and the Markov condition. Synthese, 121, 3-27.
4. Chu, T., Glymour, C., Scheines, R., & Spirtes, P. (2003). A statistical problem for inference to regulatory structure from associations of gene expression measurement with microarrays. Bioinformatics, 19, 1147-1152.
5. Glymour, C. (1999). Rabbit hunting. Synthese, 121, 55-78.
6. Glymour, C., & Cooper, G. (Eds.) (1999). Computation, causation, and discovery. Menlo Park, CA: American Association for Artificial Intelligence.
7. Halmos, P. (1950). Measure theory. New York: D. Van Nostrand Company.
8. Hoover, K. (2001). Causality in macroeconomics. Cambridge, UK: Cambridge University Press.
9. Howson, C., & Urbach, P. (1993). Scientific reasoning: The Bayesian approach (2nd edn.). La Salle, IL: Open Court Press.
10. Liljegren, S., Ditta, G., Eshed, Y., Savidge, B., Bowman, J., & Yanofsky, M. (2000). Shatterproof MADS-box genes control seed dispersal in Arabidopsis. Nature, 404, 766-770.
11. Lindsey, J. K. (1996). Parametric statistical inference. Oxford: Clarendon Press.
12. Meek, C. (1995). Strong completeness and faithfulness in Bayesian networks. In P. Besnard (Ed.), Uncertainty in artificial intelligence: Proceedings of the 11th conference (pp. 411-418). San Francisco: Morgan Kaufman Publishers.
13. Neopolitan, R. (2004). Learning Bayesian networks. Upper Saddle River, NJ: Prentice Hall.
14. Pearl, J. (1998). TETRAD and SEM. Multivariate Behavioral Research, 33, 119-128.
15. Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge: Cambridge University Press.
16. Pearson, H. (2002). Surviving a knockout blow. Nature, 415, 8-9.
17. Port, S. C. (1994). Theoretical probability for applications. New York: Wiley.
18. Robins, J. M., & Wasserman, L. (1999). On the impossibility of inferring causation from association without background knowledge. In: C. Glymour, & G. Cooper (Eds.), Computation, causation, and discovery (pp. 305-322). Menlo park, CA, Cambridge, MA: AAAI Press/The MIT Press.
19. Scarff, K., Ung, K., Nandurkar, H., Crack, P., Bird, C., & Bird, P. (2004). Targeted disruption of SPI3/Serpinb6 does not result in developmental or growth defects, leukocyte dysfunction, or susceptibility to stroke. Molecular and Cellular Biology, 24, 4075-4082.
20. Spirtes, P., Glymour, C., & Schemes, R. (1993). Causation, prediction, and search. Berlin Heidelberg New York: Springer.
21. Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search, (2nd edn.). Cambridge, MA: MIT Press.
22. Steel, D. (2005). Indeterminism and the Causal Markov Condition. British Journal for the Philosophy of Science, 56, 3-26.
23. Stirzaker, D. (2003). Elementary probability (2nd edn.). Cambridge, UK: Cambridge University Press.
24. Woodward, J. (1998). Causal independence and faithfulness. Multivariate Behavioral Research, 33, 129-148.