Identifying confounders using additive noise models

2009

Conference Paper

ei


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.

Author(s): Janzing, D. and Peters, J. and Mooij, JM. and Schölkopf, B.
Book Title: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence
Journal: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009)
Pages: 249-257
Year: 2009
Month: June
Day: 0
Editors: J Bilmes and AY Ng
Publisher: AUAI Press

Department(s): Empirical Inference
Research Project(s): Causality (Causal Inference)
Bibtex Type: Conference Paper (inproceedings)

Address: Corvallis, OR, USA
Digital: 0
Event Name: UAI 2009
Event Place: Montréal, Canada
ISBN: 978-0-9749039-5-8
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{5903,
  title = {Identifying confounders using additive noise models},
  author = {Janzing, D. and Peters, J. and Mooij, JM. and Sch{\"o}lkopf, B.},
  journal = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009)},
  booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence},
  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},
  month_numeric = {6}
}