Detection of unfaithfulness and robust causal inference

Minds and Machines

Volumn 18, Issue 2, 2008, Pages 239-271

  Zhang, Jiji ^a   Spirtes, Peter ^b  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Bayesian network; Causal inference; Epistemology of causation; Faithfulness condition; Machine learning; Uniform consistency

Indexed keywords

BAYESIAN NETWORKS; ERROR ANALYSIS; LEARNING SYSTEMS; PROBABILITY; RELIABILITY THEORY; ROBUST CONTROL;

CAUSAL INFERENCE; EPISTEMOLOGY OF CAUSATION; FAITHFULNESS CONDITIONS; UNIFORM CONSISTENCY;

MARKOV PROCESSES;

EID: 44349095903     PISSN: 09246495     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11023-008-9096-4     Document Type: Article

References (40)

1. Artzenius, F. (1992). The common cause principle. In D. Hull & K. Okruhlik (Eds.), PSA Procceding (Vol. 2, pp. 227-237). East Lansing, MI: PSA.
2. Cartwright, N. (1989). Nature's capacities and their measurement. Oxford: Clarendon Press.
3. Cartwright, N. (1999). The dappled world. Cambridge: Cambridge University Press.
4. Cartwright, N. (2001). What is wrong with Bayes nets? The Monist, 242-264.
5. Chickering, D. M. (2002). Optimal structure Identification with greedy search. Journal of Machine Learning Research, 3, 507-554.
6. Cooper, G. (1999). An overview of the representation and discovery of causal relationships using Bayesian networks. In C. Glymour & G. F. Cooper (Eds.), Computation, causation, and discovery (pp. 3-62). Cambridge, MA: MIT Press.
7. Dawid, P. (2002). Influence diagrams for causal modelling and inference. International Statistical Review, 70, 161-189.
8. Glymour, C. (1980). Theory and evidence. Princeton: Princeton University Press.
9. Glymour, C. (2007). Learning the structure of deterministic systems. In A. Gopnik, L. Schulz (Eds.), Causal learning: psychology, philosophy and computation (Chap. 14). Oxford University Press.
10. Hausman, D. M., & Woodward, J. (1999). Independence, invariance and the causal Markov condition. British Journal for the Philosophy of Science, 50, 521-583.
11. Hausman, D. M., & Woodward J. (2004). Manipulation and causal Markov condition. Philosophy of Science, 71, 846-856.
12. Heckerman, D., Meek, C., & Cooper, G. (1999). An Bayesian approach to causal discovery. In: C. Glymour & G. F. Cooper (Eds.), Computation, causation, and discovery (Chap. 4). Cambridge, MA: MIT Press.
13. Hesslow, G. (1976). Two notes on the probabilistic approach to causality. Philosophy of Science, 43, 290-292.
14. Hitchcock, C. (2001a). The intransitivity of causation revealed in equations and graphs. Journal of Philosophy, 98, 273-299.
15. Hitckcock, C. (2001b). A tale of two effects. Philosophical Review, 110, 361-396.
16. Hoover, K. D. (2001). Causality in macroeconomics. Cambridge: Cambridge University Press.
17. Mayo, D. (1996). Error and the growth of experimental knowledge. Chicago: University Of Chicago Press.
18. Mayo, D., & Spanos A. (2004). Methodology in practice: Statistical misspecification testing. Philosophy of Science, 71, 1007-1025.
19. McDermott, M. (1995). Redundant causation. British Journal for the Philosophy of Science, 40, 523-544.
20. Meek, C. (1995a). Causal inference and causal explanation with background knowledge. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (pp. 403-411). Morgan Kaufmann.
21. Meek, C. (1995b). Strong completeness and faithfulness in Bayesian networks. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (pp. 411-418). San Francisco: Morgan Kaufmann.
22. Pearl, J. (1988). Probabilistic reasoning in intelligence systems. San Mateo, California: Morgan Kaufmann.
23. Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge, UK: Cambridge University Press.
24. Ramsey, J., Spirtes, P., & Zhang J. (2006). Adjacency-faithfulness and conservative causal inference. In Proceedings of 22nd Conference on Uncertainty in Artificial Intelligence (pp. 401-408). Oregon: AUAI Press.
25. Richardson, T., & Spirtes, P. (2002). Ancestral Markov graphical models. Annals of Statistics, 30(4), 962-1030.
26. Robins, J. M., Scheines, R., Spirtes, P., & Wasserman, L. (2003). Uniform consistency in causal inference. Biometrika, 90(3), 491-515.
27. Shimizu, S., Hoyer, P., Hyvarinen, A., & Kerminen, A. (2006). A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 2003-2030.
28. Sober, E. (1987). The principle of the common cause. In J. Fetzer (Ed.), Probability and causation: Essays in honor of Wesley Salmon (pp. 211-28). Dordrecht: Redel.
29. Spanos, A. (2006). Revisiting the omitted variables argument: Substantive vs. statistical adequacy. Journal of Economic Methodology (forthcoming).
30. Spirtes, P., Glymour, C., & Scheines, R. (1991). An algorithm for fast recovery of sparse causal graphs. Social Science Computer Review, 9, 62-72.
31. Spirtes, P., Glymour, C., & Scheines R. (1993). Causation, prediction and search. New York: Springer-Verlag. (2000, 2nd ed.) Cambridge, MA: MIT Press.
32. Spohn, W. (2000). Bayesian nets are all there is to causal dependence. In M. C. Galavotti et al. (Eds.), Stochastic dependence and causality (pp. 157-172). CSLI Publications.
33. Steel, D. (2006). Homogeneity, selection, and the faithfulness condition. Minds and Machines, 16, 303-317.
34. Verma, T., & Pearl J. (1990). Equivalence and synthesis of causal models. In Proceedings of 6th Conference on Uncertainty in Artificial Intelligence (pp. 220-227).
35. Woodward, J. (1998). Causal independence and faithfulness. Multivariate Behavioral Research, 33, 129-148.
36. Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford and New York: Oxford University Press.
37. Zhang, J. (2006a). Underdetermination of causal hypotheses by statistical data. Technical report, Department of Philosophy, Carnegie Mellon University.
38. Zhang, J. (2006b). Causal inference and reasoning in causally insufficient systems. PhD dissertation, Department of Philosophy, Carnegie Mellon University. Available at http://www.hss.caltech.edu/~jiji/dissertation. pdf.
39. Zhang, J. (2008). Error probabilities for inference of causal directions. Synthese (forthcoming)
40. Zhang, J., & Spirtes P. (2003). Strong faithfulness and uniform consistency in causal inference. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (pp. 632-639). Morgan Kaufmann.