title: Modeling confounding by half-sibling regression.
abstract: We describe a method for removing the effect of confounders 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, discussing both independent and identically distributed as well as time series data, respectively, and illustrate the potential of the method in a challenging astronomy application.
authors.affiliation: Department of Empirical Inference, MPI for Intelligent Systems, Max Planck Institute for Intelligent Systems, 72076 Tuebingen, Germany; bs@tuebingen.mpg.de.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Schölkopf
authors.affiliation: Center for Cosmology and Particle Physics, New York University, New York, NY 10003.
authors.forename: David W
authors.initials: DW
authors.lastname: Hogg
authors.affiliation: Center for Cosmology and Particle Physics, New York University, New York, NY 10003.
authors.forename: Dun
authors.initials: D
authors.lastname: Wang
authors.affiliation: Center for Cosmology and Particle Physics, New York University, New York, NY 10003.
authors.forename: Daniel
authors.initials: D
authors.lastname: Foreman-Mackey
authors.affiliation: Department of Empirical Inference, MPI for Intelligent Systems, Max Planck Institute for Intelligent Systems, 72076 Tuebingen, Germany;
authors.forename: Dominik
authors.initials: D
authors.lastname: Janzing
authors.affiliation: Department of Empirical Inference, MPI for Intelligent Systems, Max Planck Institute for Intelligent Systems, 72076 Tuebingen, Germany;
authors.forename: Carl-Johann
authors.initials: CJ
authors.lastname: Simon-Gabriel
authors.affiliation: Department of Empirical Inference, MPI for Intelligent Systems, Max Planck Institute for Intelligent Systems, 72076 Tuebingen, Germany;
authors.forename: Jonas
authors.initials: J
authors.lastname: Peters
keywords: astronomy; causal inference; exoplanet detection; machine learning; systematic error modeling