PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On Causal Discovery with Cyclic Additive Noise Models
Joris Mooij, Dominik Janzing, Tom Heskes and Bernhard Schölkopf
In: Advances in Neural Information Processing Systems 24 (NIPS*2011), December 12-14, 2011, Granada, Spain.

Abstract

We study a particular class of cyclic causal models, where each variable is a (possibly nonlinear) function of its parents and additive noise. We prove that the causal graph of such models is generically identifiable in the bivariate, Gaussian-noise case. We also propose a method to learn such models from observational data. In the acyclic case, the method reduces to ordinary regression, but in the more challenging cyclic case, an additional term arises in the loss function, which makes it a special case of nonlinear independent component analysis. We illustrate the proposed method on synthetic data.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:8965
Deposited By:Joris Mooij
Deposited On:21 February 2012