Modeling confounding by half-sibling regression

Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl Johann Simon-Gabriel, Jonas Peters

    Research output: Contribution to journalArticle

    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.

    Original languageEnglish (US)
    Pages (from-to)7391-7398
    Number of pages8
    JournalProceedings of the National Academy of Sciences of the United States of America
    Volume113
    Issue number27
    DOIs
    StatePublished - Jul 5 2016

    Keywords

    • Astronomy
    • Causal inference
    • Exoplanet detection
    • Machine learning
    • Systematic error modeling

    ASJC Scopus subject areas

    • General

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  • Cite this

    Schölkopf, B., Hogg, D. W., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C. J., & Peters, J. (2016). Modeling confounding by half-sibling regression. Proceedings of the National Academy of Sciences of the United States of America, 113(27), 7391-7398. https://doi.org/10.1073/pnas.1511656113