@inproceedings{6767,
  title = {Probabilistic latent variable models for distinguishing between cause and effect},
  journal = {Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010},
  booktitle = {Advances in Neural Information Processing Systems 23},
  abstract = {We propose a novel method for inferring whether X causes Y or vice versa from joint observations of X and Y. The basic idea is to model the observed data using probabilistic
  latent variable models, which incorporate the effects of unobserved noise. To this end, we consider the hypothetical effect variable to be a function of the hypothetical cause variable and an independent noise term (not necessarily additive). An important novel aspect of our work is that we do not restrict the model class, but instead put general non-parametric priors on this function and on the distribution of the cause. The causal direction can then
  be inferred by using standard Bayesian model selection. We evaluate our approach on synthetic data and real-world data and report encouraging results.},
  pages = {1687-1695},
  editors = {J Lafferty and CKI Williams and J Shawe-Taylor and RS Zemel and A Culotta},
  publisher = {Curran},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Red Hook, NY, USA},
  year = {2010},
  slug = {6767},
  author = {Mooij, JM. and Stegle, O. and Janzing, D. and Zhang, K. and Sch{\"o}lkopf, B.}
}