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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 PetersProceedings of the 32nd International Conference on Machine Learning, PMLR 37:2218-2226, 2015.
Abstract
We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to ashalf-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.
Cite this Paper
BibTeX
@InProceedings{pmlr-v37-scholkopf15, title = {Removing systematic errors for exoplanet search via latent causes}, author = {Schölkopf, Bernhard and Hogg, David and Wang, Dun and Foreman-Mackey, Dan and Janzing, Dominik and Simon-Gabriel, Carl-Johann and Peters, Jonas}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {2218--2226}, year = {2015}, editor = {Bach, Francis and Blei, David}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v37/scholkopf15.pdf}, url = {https://proceedings.mlr.press/v37/scholkopf15.html}, abstract = {We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to ashalf-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.}} Endnote
%0 Conference Paper%T Removing systematic errors for exoplanet search via latent causes%A Bernhard Schölkopf%A David Hogg%A Dun Wang%A Dan Foreman-Mackey%A Dominik Janzing%A Carl-Johann Simon-Gabriel%A Jonas Peters%B Proceedings of the 32nd International Conference on Machine Learning%C Proceedings of Machine Learning Research%D 2015%E Francis Bach%E David Blei%F pmlr-v37-scholkopf15%I PMLR%P 2218--2226%U https://proceedings.mlr.press/v37/scholkopf15.html%V 37%X We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to ashalf-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. RIS
TY - CPAPERTI - Removing systematic errors for exoplanet search via latent causesAU - Bernhard SchölkopfAU - David HoggAU - Dun WangAU - Dan Foreman-MackeyAU - Dominik JanzingAU - Carl-Johann Simon-GabrielAU - Jonas PetersBT - Proceedings of the 32nd International Conference on Machine LearningDA - 2015/06/01ED - Francis BachED - David BleiID - pmlr-v37-scholkopf15PB - PMLRDP - Proceedings of Machine Learning ResearchVL - 37SP - 2218EP - 2226L1 - http://proceedings.mlr.press/v37/scholkopf15.pdfUR - https://proceedings.mlr.press/v37/scholkopf15.htmlAB - We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to ashalf-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.ER - APA
Schölkopf, B., Hogg, D., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C. & Peters, J.. (2015). Removing systematic errors for exoplanet search via latent causes.Proceedings of the 32nd International Conference on Machine Learning, inProceedings of Machine Learning Research 37:2218-2226 Available from https://proceedings.mlr.press/v37/scholkopf15.html.