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Learning causality by identifying common effects with kernel-based dependence measures

2007

Conference Paper

ei


We describe a method for causal inference that measures the strength of statistical dependence by the Hilbert-Schmidt norm of kernel-based conditional cross-covariance operators. We consider the increase of the dependence of two variables X and Y by conditioning on a third variable Z as a hint for Z being a common effect of X and Y. Based on this assumption, we collect "votes" for hypothetical causal directions and orient the edges according to the majority vote. For most of our experiments with artificial and real-world data our method has outperformed the conventional constraint-based inductive causation (IC) algorithm.

Author(s): Sun, X. and Janzing, D.
Book Title: ESANN 2007
Journal: Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)
Pages: 453-458
Year: 2007
Month: April
Day: 0
Publisher: D-Side

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 15th European Symposium on Artificial Neural Networks
Event Place: Brugge, Belgium

Address: Evere, Belgium
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4455,
  title = {Learning causality by identifying common effects with kernel-based dependence measures},
  author = {Sun, X. and Janzing, D.},
  journal = {Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)},
  booktitle = {ESANN 2007},
  pages = {453-458},
  publisher = {D-Side},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Evere, Belgium},
  month = apr,
  year = {2007},
  month_numeric = {4}
}