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Modeling Confounding by Half-Sibling Regression

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If you have a question about this talk, please contact Zoubin Ghahramani.

SHORT TALK (20 minutes)

We describe a method for removing the effect of confounders to re-construct 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 in the field of exoplanet detection.

This talk is part of the Machine Learning @ CUED series.

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