Modeling confounding by half-sibling regression

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

doi:10.1073/pnas.1511656113

Modeling confounding by half-sibling regression

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

12 Citations (Scopus)

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 language	English
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	113
Issue number	27
Pages (from-to)	7391-7398
Number of pages	8
ISSN	0027-8424
DOIs	https://doi.org/10.1073/pnas.1511656113
Publication status	Published - 5 Jul 2016
Externally published	Yes

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Journal Article

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AU - Schölkopf, Bernhard

AU - Hogg, David W

AU - Wang, Dun

AU - Foreman-Mackey, Daniel

AU - Janzing, Dominik

AU - Simon-Gabriel, Carl-Johann

AU - Peters, Jonas

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Modeling confounding by half-sibling regression

Abstract

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