Modeling confounding by half-sibling regression

被引：32

作者：

Schoelkopf, Bernhard ^{[1
]}

Hogg, David W. ^{[2
]}

Wang, Dun ^{[2
]}

Foreman-Mackey, Daniel ^{[2
]}

Janzing, Dominik ^{[1
]}

Simon-Gabriel, Carl-Johann ^{[1
]}

Peters, Jonas ^{[1
]}

机构：

[1] Max Planck Inst Intelligent Syst, Dept Empir Inference, D-72076 Tubingen, Germany

[2] NYU, Ctr Cosmol & Particle Phys, 550 1St Ave, New York, NY 10003 USA

来源：

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | 2016年 / 113卷 / 27期

关键词：

machine learning; causal inference; astronomy; exoplanet detection; systematic error modeling; UNWANTED VARIATION; ERROR-CORRECTION; EXPRESSION DATA; ASSOCIATION; DISCOVERY;

D O I：

10.1073/pnas.1511656113

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页码：7391 / 7398

页数：8

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