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Abstract:We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as half-sibling regression, is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification and illustrate the potential of the method in a challenging astronomy application.
* Extended version of a paper appearing in the Proceedings of the 32nd
International Conference on Machine Learning, Lille, France, 2015