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.
Originalsprog | Udefineret/Ukendt |
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Titel | Proceedings of the 32nd International Conference on Machine Learning (ICML) |
Antal sider | 9 |
Publikationsdato | 2015 |
Sider | 2218-2226 |
Status | Udgivet - 2015 |
Udgivet eksternt | Ja |