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