Almost optimal intervention sets for causal discovery

Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

Volumn , Issue , 2008, Pages 161-168

  Eberhardt, Frederick ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b WASHINGTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CAUSAL GRAPH; DIRECTED ACYCLIC GRAPHS; MARKOV EQUIVALENCE CLASS; OPTIMAL INTERVENTION; WORST CASE; WORST-CASE ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; EXPERIMENTS; FUNCTIONS; OPTIMIZATION;

EQUIVALENCE CLASSES;

EID: 80053288073     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. D. M. Chickering (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research, 3:507-554.
2. G. Cooper and C. Yoo (1999). Causal discovery from a mixture of data. In Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence, 116-125, San Francisco, CA.: Morgan Kaufmann.
3. F. Eberhardt, C. Glymour, and R. Scheines (2005). On the number of experiments suficient and in the worst case necessary to identify all causal relations among N variables. In F. Bacchus and T. Jaakkola (ed.), Proceedings of the 21 st Conference on Uncertainty and Artificial Intelligence, 178-184. AUAI Press, Corvallis, Oregon.
4. F. Eberhardt (2007). Causation and Intervention, PhD thesis, Carnegie Mellon University, Pittsburgh, PA.
5. J. Folkman (1970). Graphs with monochromatic complete subgraphs in every edge coloring. SIAM Journal on Applied Mathematics, 18, No. 1:19-24.
6. M. X. Goemans and D. P. Williamson (1995). Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM, 42,6:1115-1145.
7. C. Meek (1995). Causal inference and causal explanation with background knowledge. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, 403-418, Montreal, QU.: Morgan Kaufmann.
8. S. Meganck, B. Manderick, and P. Leray (2005). A decision theoretic approach to learning bayesian networks. Technical report, Vrije Universiteit Brussels.
9. K. P. Murphy (2001). Active learning of causal bayes net structure. Technical report, Department of Computer Science, U.C. Berkeley.
10. J. Pearl (2000). Causality. Oxford University Press.
11. S. Shimizu, P. O. Hoyer, A. Hyvarinen, and A. J. Kerminen (2006). A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research 7:2003-2030. (Pubitemid 44497456)
12. P. Spirtes, C. Glymour, and R. Scheines (2000). Causation, Prediction and Search. MIT Press, 2 edition.
13. S. Tong and D. Koller (2001). Active learning for structure in bayesian networks. In Proceedings of the International Joint Conference on Artificial Intelligence.