PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Identifying confounders using aditive noise models
Dominik / DJ Janzing, Jonas / JP Peters, Joris / JM Mooij and Bernhard / BS Schoelkopf
In: 25th Conference on Uncertainty in Artificial Intelligence, 18-21 June 2009, Corvallis, OR, USA.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6827
Deposited By:Dominik Janzing
Deposited On:08 March 2010