@inproceedings{6387,
  title = {Identifying Cause and Effect on Discrete Data using Additive Noise Models},
  journal = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010},
  abstract = {Inferring the causal structure of a set of random
  variables from a finite sample of the
  joint distribution is an important problem
  in science. Recently, methods using additive
  noise models have been suggested to approach
  the case of continuous variables. In
  many situations, however, the variables of interest
  are discrete or even have only finitely
  many states. In this work we extend the notion
  of additive noise models to these cases.
  Whenever the joint distribution P(X;Y ) admits
  such a model in one direction, e.g. Y =
  f(X) + N; N ? X, it does not admit the
  reversed model X = g(Y ) + ~N ; ~N ? Y as
  long as the model is chosen in a generic way.
  Based on these deliberations we propose an
  efficient new algorithm that is able to distinguish
  between cause and effect for a finite
  sample of discrete variables. We show that
  this algorithm works both on synthetic and
  real data sets.},
  pages = {597-604},
  editors = {YW Teh and M Titterington},
  publisher = {JMLR},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = may,
  year = {2010},
  slug = {6387},
  author = {Peters, J. and Janzing, D. and Sch{\"o}lkopf, B.},
  month_numeric = {5}
}