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Probabilistic latent variable models for distinguishing between cause and effect

2010

Conference Paper

ei


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.

Author(s): Mooij, JM. and Stegle, O. and Janzing, D. and Zhang, K. and Schölkopf, B.
Book Title: Advances in Neural Information Processing Systems 23
Journal: Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010
Pages: 1687-1695
Year: 2010
Day: 0
Editors: J Lafferty and CKI Williams and J Shawe-Taylor and RS Zemel and A Culotta
Publisher: Curran

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 24th Annual Conference on Neural Information Processing Systems (NIPS 2010)
Event Place: Vancouver, BC, Canada

Address: Red Hook, NY, USA
Digital: 0
ISBN: 978-1-617-82380-0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{6767,
  title = {Probabilistic latent variable models for distinguishing between cause and effect},
  author = {Mooij, JM. and Stegle, O. and Janzing, D. and Zhang, K. and Sch{\"o}lkopf, B.},
  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},
  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}
}