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Removing systematic errors for exoplanet search via latent causes

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arxiv 1505.03036 v1 pith:XDMF73CW submitted 2015-05-12 stat.ML astro-ph.EPastro-ph.IMcs.LG

Bernhard Schölkopf , David W. Hogg , Dun Wang , Daniel Foreman-Mackey , Dominik Janzing , Carl-Johann Simon-Gabriel , Jonas Peters This is my paper · ORCID
classification stat.MLastro-ph.EPastro-ph.IMcs.LG
keywords methodlatentremovingadditiveapplicationastronomycausalcauses
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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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Cited by 1 Pith paper

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  1. Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning

    astro-ph.GA 2024-12 conditional novelty 5.0 of 10

    Using causal machine learning on IllustrisTNG, environment is estimated to suppress star formation by up to ~100x at z=0 but to boost it by ~10x at z~1 and more at higher redshifts.

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