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Telling cause from effect based on high-dimensional observations

2010

Conference Paper

ei


We describe a method for inferring linear causal relations among multi-dimensional variables. The idea is to use an asymmetry between the distributions of cause and effect that occurs if the covariance matrix of the cause and the structure matrix mapping the cause to the effect are independently chosen. The method applies to both stochastic and deterministic causal relations, provided that the dimensionality is sufficiently high (in some experiments, 5 was enough). It is applicable to Gaussian as well as non-Gaussian data.

Author(s): Janzing, D. and Hoyer, P. and Schölkopf, B.
Book Title: Proceedings of the 27th International Conference on Machine Learning
Pages: 479-486
Year: 2010
Month: June
Day: 0
Editors: J F{\"u}rnkranz and T Joachims
Publisher: International Machine Learning Society

Department(s): Empirical Inference
Research Project(s): Causality (Causal Inference)
Bibtex Type: Conference Paper (inproceedings)

Event Name: ICML 2010
Event Place: Haifa, Israel

Address: Madison, WI, USA
ISBN: 978-1-605-58907-7
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{6501,
  title = {Telling cause from effect based on high-dimensional observations},
  author = {Janzing, D. and Hoyer, P. and Sch{\"o}lkopf, B.},
  booktitle = {Proceedings of the 27th International Conference on Machine Learning},
  pages = {479-486},
  editors = {J F{\"u}rnkranz and T Joachims},
  publisher = {International Machine Learning Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Madison, WI, USA},
  month = jun,
  year = {2010},
  month_numeric = {6}
}