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
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
收藏
页码:7391 / 7398
页数:8
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