@article{54c1c4af71d243ffadc59ab8ae511d3a,
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.",
keywords = "Astronomy, Causal inference, Exoplanet detection, Machine learning, Systematic error modeling",
author = "Bernhard Sch{\"o}lkopf and Hogg, {David W.} and Dun Wang and Daniel Foreman-Mackey and Dominik Janzing and Simon-Gabriel, {Carl Johann} and Jonas Peters",
note = "Funding Information: We thank Stefan Harmeling, James McMurray, Oliver Stegle, and Kun Zhang for helpful discussion, and the anonymous reviewers for helpful suggestions and references. D.W.H., D.W., and D.F.-M. were partially supported by NSF (IIS-1124794) and NASA (NNX12AI50G) and the Moore-Sloan Data Science Environment at NYU. C.-J.S.-G. was supported by a Google Europe Doctoral Fellowship in Causal Inference. J.P. was supported by the Max Planck ETH Center for Learning Systems.",
year = "2016",
month = jul,
day = "5",
doi = "10.1073/pnas.1511656113",
language = "English (US)",
volume = "113",
pages = "7391--7398",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
publisher = "National Academy of Sciences",
number = "27",
}