@inproceedings{4454,
  title = {Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions},
  journal = {Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)},
  booktitle = {Proceedings of the 15th European Symposium on Artificial Neural Networks
  },
  abstract = {We propose a method to evaluate the complexity of probability measures from data that is based on a reproducing kernel Hilbert space seminorm of the logarithm of conditional probability densities. The motivation is to provide a tool for a causal inference method which assumes that conditional probabilities for effects given their causes are typically simpler and smoother than vice-versa. We present experiments with toy data where the quantitative results are consistent with our intuitive understanding of complexity and smoothness. Also in some examples with real-world data the probability measure corresponding to the true causal direction turned out to be less complex than those of the reversed order.},
  pages = {441-446},
  editors = {M Verleysen},
  publisher = {D-Side Publications},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Evere, Belgium},
  month = apr,
  year = {2007},
  slug = {4454},
  author = {Sun, X. and Janzing, D. and Sch{\"o}lkopf, B.},
  month_numeric = {4}
}