@inproceedings{6620,
  title = {Inferring deterministic causal relations},
  journal = {Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Sixth Conference (UAI 2010)},
  booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence},
  abstract = {We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints
  to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We
  provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.},
  pages = {143-150},
  editors = {P Gr{\"u}nwald and P Spirtes},
  publisher = {AUAI Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Corvallis, OR, USA},
  month = jul,
  year = {2010},
  slug = {6620},
  author = {Daniusis, P. and Janzing, D. and Mooij, J. and Zscheischler, J. and Steudel, B. and Zhang, K. and Sch{\"o}lkopf, B.},
  month_numeric = {7}
}