ML Anthology

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
ICML 2015 pp. 2218-2226
/icml/2015/scholkopf2015icml-removing/

Abstract

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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Cite

Text

Schölkopf et al. "Removing Systematic Errors for Exoplanet Search via Latent Causes." International Conference on Machine Learning, 2015.

Markdown

[Schölkopf et al. "Removing Systematic Errors for Exoplanet Search via Latent Causes." International Conference on Machine Learning, 2015.](https://mlanthology.org/icml/2015/scholkopf2015icml-removing/)

BibTeX

@inproceedings{scholkopf2015icml-removing,
  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 = {International Conference on Machine Learning},
  year      = {2015},
  pages     = {2218-2226},
  volume    = {37},
  url       = {https://mlanthology.org/icml/2015/scholkopf2015icml-removing/}
}