Learning causal structure from overlapping variable sets

Journal of Machine Learning Research

Volumn 9, Issue , 2010, Pages 860-867

  Triantafillou, Sofia ^a   Tsamardinos, Ioannis ^a   Tollis, Ioannis G ^a  

^a UNIVERSITY OF CRETE   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

CAUSAL GRAPH; CAUSAL RELATIONS; DATA SETS; EMPIRICAL EVALUATIONS; ORDERS OF MAGNITUDE; SAT PROBLEMS;

ARTIFICIAL INTELLIGENCE;

ALGORITHMS;

EID: 84860667650     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (10)

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2. Jackson, P. and Sheridan, D. (2005). Clause form conversions for boolean circuits. In SAT 2004.
3. Neapolitan, R. E. (2003). Learning Bayesian Networks. Prentice Hall.
4. Richardson, T. and Spirtes, P. (2002). Ancestral graph Markov models. Annals of Statistics, 30(4):962-1030.
5. Spirtes, P., Glymour, C., and Scheines, R. (2000). Causation, Prediction, and Search. MIT Press, Cambridge, MA, 2nd edition.
6. Spirtes, P. and Richardson, T. (1997). A polynomial time algorithm for determining DAG equivalence in the presence of latent variables and selection bias.
7. Tillman, R. E., Danks, D., and Glymour, C. (2008). Integrating locally learned causal structures with overlapping variables. In NIPS.
8. Triantafillou, S. (2010). Learning causal structure from overlapping variable sets. Master's thesis, University of Crete, Heraklion.
9. Tsamardinos, I. and Triantafillou, S. (2010). The possibility of integrative causal analysis: Learning from different datasets and studies. Journal of Engineering Intelligent Systems, to appear.
10. Zhang, J. (2008). On the completeness of orientation rules for causal discovery in the presence of latent con-founders and selection bias. Artif. Intell., 172(16-17):1873-1896.