Confounder Detection in High-Dimensional Linear Models Using First Moments of Spectral Measures
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AbstractIn this letter, we study the confounder detection problem in the linear model, where the target variable Y is predicted using its n potential causes Xn=(x1,…,xn)T. Based on an assumption of a rotation-invariant generating process of the model, recent study shows that the spectral measure induced by the regression coefficient vector with respect to the covariance matrix of Xn is close to a uniform measure in purely causal cases, but it differs from a uniform measure characteristically in the presence of a scalar confounder. Analyzing spectral measure patterns could help to detect confounding. In this letter, we propose to use the first moment of the spectral measure for confounder detection. We calculate the first moment of the regression vector–induced spectral measure and compare it with the first moment of a uniform spectral measure, both defined with respect to the covariance matrix of Xn. The two moments coincide in nonconfounding cases and differ from each other in the presence of confounding. This statistical causal-confounding asymmetry can be used for confounder detection. Without the need to analyze the spectral measure pattern, our method avoids the difficulty of metric choice and multiple parameter optimization. Experiments on synthetic and real data show the performance of this method.
All Author(s) ListFurui Liu, Laiwan Chan
Journal nameNeural Computation
Year2018
Month8
Volume Number30
Issue Number8
PublisherMIT Press
Place of PublicationUSA
Pages2284 - 2318
ISSN0899-7667
eISSN1530-888X
LanguagesEnglish-United States

Last updated on 2021-14-01 at 00:43