Identifiability of causal graphs using functional models

Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

Volumn , Issue , 2011, Pages 589-598

  Peters, Jonas ^a   Mooij, Joris M ^b   Janzing, Dominik ^a   Schölkop, Bernhard ^a  

^a MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^b RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

CAUSAL GRAPH; DATA SUPPORT; FUNCTIONAL MODEL; IDENTIFIABILITY; JOINT DISTRIBUTIONS; LINEAR FUNCTIONAL; MARKOV CONDITION; MARKOV EQUIVALENCES;

GRAPH ALGORITHMS;

EID: 80053168312     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. W. P. Bergsma. Testing conditional independence for continuous random variables, 2004. EURANDOMreport 2004-049.
2. G. Darmois. Analyse des liaisons de probabilité. In Proceedings of Intern. Statistics Conferences 1947, volume IIIA, page 231, Washington (D.C), 1951.
3. K. Fukumizu, A. Gretton, X. Sun, and B. Schölkopf. Kernel measures of conditional dependence. In NIPS 20, pages 489-496, Cambridge, 2008. MIT Press.
4. A. Gretton, K. Fukumizu, C. H. Teo, L. Song, B. Schölkopf, and A. Smola. A kernel statistical test of independence. In NIPS 20, pages 585-592, Cambridge, 2008. MIT Press.
5. P. Hoyer, D. Janzing, J. Mooij, J. Peters, and B. Schölkopf. Nonlinear causal discovery with additive noise models. In NIPS 21, pages 689-696, Vancouver, Canada, 2009. MIT Press.
6. D. Janzing and B. Schölkopf. Causal inference using the algorithmic Markov condition. IEEE Trans. on Information Theory, 56(10):5168-5194, 2010.
7. D. Janzing and B. Steudel. Justifying additive-noisemodel based causal discovery via algorithmic information theory. Open Systems and Information Dynamics, 17:189-212, 2010.
8. D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
9. J. Lemeire and E. Dirkx. Causal models as minimal descriptions of multivariate systems. http://parallel.vub.ac.be/~jan/, 2006.
10. C. Meek. Causal Inference and Causal Explanation with Background Knowledge. In UAI 11, pages 403-441, 1995.
11. J. Mooij, D. Janzing, J. Peters, and B. Schölkopf. Regression by dependence minimization and its application to causal inference. In ICML 26, pages 745-752, Montreal, 2009. Omnipress.
12. J. Pearl. Causality: Models, reasoning, and inference. Cambridge University Press, 2nd edition, 2009.
13. J. Peters, D. Janzing, and B. Schölkopf. Identifying Cause and Effect on Discrete Data using Additive Noise Models. In AIStats 13, pages 597-604, 2010.
14. S. Shimizu, P. O. Hoyer, A. Hyvärinen, and A. J. Kerminen. A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7:2003-2030, 2006. (Pubitemid 44497456)
15. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT Press, 2. edition, 2001.
16. R. Tillman, A. Gretton, and P. Spirtes. Nonlinear directed acyclic structure learning with weakly additive noise models. In NIPS 22, Vancouver, 2009.
17. J. Zhang and P. Spirtes. Detection of unfaithfulness and robust causal inference. In LSE-Pitt Conference: Confirmation, Induction and Science, 2007.
18. K. Zhang and A. Hyvärinen. On the identifiability of the post-nonlinear causal model. In UAI 25, 2009.