Removing systematic errors for exoplanet search via latent causes


Bernhard Schölkopf, David Hogg, Dun Wang, Dan Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters ;
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2218-2226, 2015.


We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as \em 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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