Removing systematic errors for exoplanet search via latent causes

Schölkopf, Bernhard, Hogg, David W., Wang, Dun, Foreman-Mackey, Daniel, Janzing, Dominik, Simon-Gabriel, Carl-Johann, Peters, Jonas

arXiv.org Machine Learning 

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.

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