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Modeling confounding by half-sibling regression
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DOI：10.1073/pnas.1511656113.png)
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Abstract
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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:
machine learning
causal inference
astronomy
exoplanet detection
systematic error modeling
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