Inferring deterministic causal relations

Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

Volumn , Issue , 2010, Pages 143-150

  Daniufisis, Povilas ^a,b   Janzing, Dominik ^a   Mooij, Joris ^a   Zscheischler, Jakob ^a   Steudel, Bastian ^c   Zhang, Kun ^a   Schölkopf, Bernhard ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b VILNIUS UNIVERSITY   (Lithuania)

^c MAX PLANCK INSTITUTE FOR MATHEMATICS IN THE SCIENCES   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; PROBABILITY DISTRIBUTIONS;

CAUSAL INFERENCES; CAUSAL RELATIONS; DEPENDENCE STRUCTURES; DIFFERENT DOMAINS; INFORMATION GEOMETRY; LOW NOISE; PROBABILITY DENSITIES; REAL-WORLD;

PROBABILITY DENSITY FUNCTION;

EID: 80053150280     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. S. Amari. Information geometry on hierarchy of probability distributions. IEEE Transactions on Information Theory, 47(5):1701-1711, 2001. (Pubitemid 32644689)
2. P. Hoyer, D. Janzing, J. Mooij, J. Peters, and B Schölkopf. Nonlinear causal discovery with additive noise models. In Proceedings of the conference Neural Information Processing Systems (NIPS) 2008, Vancouver, Canada, 2009. MIT Press.
3. D. Janzing and B. Schölkopf. Causal inference using the algorithmic Markov condition. to appear in IEEE Transactions on Information Theory http://arxiv.org/abs/0804.3678, 2008.
4. D. Janzing, P. Hoyer, and B. Schölkopf. Telling cause from effect based on high-dimensional observations. http://arxiv.org/abs/0909.4386, 2009.
5. A. Kraskov, H. Stoegbauer, and P. Grassberger. Estimating Mutual Information. 2003. URL http://arxiv.org/abs/cond-mat/0305641v1.
6. J. Lemeire and E. Dirkx. Causal models as minimal descriptions of multivariate systems. http://parallel.vub.ac.be/fijan/, 2006.
7. J. Mooij and D. Janzing. Distinguishing between cause and efiect. Journal of Machine Learning Research W&CP, 6:147-156, 2010.
8. J. Pearl. Causality. Cambridge University Press, 2000.
9. P. Spirtes, C. Glymour, and R. Scheines. Causation, prediction, and search (Lecture notes in statistics). SpringerVerlag, New York, NY, 1993.
10. K. Zhang and A. Hyvärinen. On the identifiability of the post-nonlinear causal model. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, Montreal, Canada, 2009.