@inproceedings{5903,
  title = {Identifying confounders using additive noise models},
  journal = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009)},
  booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence},
  abstract = {We propose a method for inferring the existence
  of a latent common cause ("confounder")
  of two observed random variables.
  The method assumes that the two effects of
  the confounder are (possibly nonlinear) functions
  of the confounder plus independent, additive
  noise. We discuss under which conditions
  the model is identifiable (up to an arbitrary
  reparameterization of the confounder)
  from the joint distribution of the effects. We
  state and prove a theoretical result that provides
  evidence for the conjecture that the
  model is generically identifiable under suitable
  technical conditions. In addition, we
  propose a practical method to estimate the
  confounder from a finite i.i.d. sample of the
  effects and illustrate that the method works
  well on both simulated and real-world data.},
  pages = {249-257},
  editors = {J Bilmes and AY Ng},
  publisher = {AUAI Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Corvallis, OR, USA},
  month = jun,
  year = {2009},
  slug = {5903},
  author = {Janzing, D. and Peters, J. and Mooij, JM. and Sch{\"o}lkopf, B.},
  month_numeric = {6}
}