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MERLiN: Mixture Effect Recovery in Linear Networks

Weichwald, S; Grosse-Wentrup, M; Gretton, A; (2016) MERLiN: Mixture Effect Recovery in Linear Networks. IEEE Journal of Selected Topics in Signal Processing , 10 (7) pp. 1254-1266. 10.1109/JSTSP.2016.2601144. Green open access

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Abstract

Causal inference concerns the identification of cause-effect relationships between variables, e.g., establishing whether a stimulus affects activity in a certain brain region. The observed variables themselves often do not constitute meaningful <;italic>causal<;/italic> variables, however, and linear combinations need to be considered. In electroencephalographic studies, for example, one is not interested in establishing cause-effect relationships between electrode signals (the observed variables), but rather between cortical signals (the causal variables) which can be recovered as linear combinations of electrode signals. We introduce Mixture Effect Recovery in Linear Networks (MERLiN), a family of causal inference algorithms that implement a novel means of constructing causal variables from non-causal variables. We demonstrate through application to EEG data how the basic MERLiN algorithm can be extended for application to different (neuroimaging) data modalities. Given an observed linear mixture, the algorithms can recover a causal variable that is a linear effect of another given variable. That is, MERLiN allows us to recover a cortical signal that is affected by activity in a certain brain region, while not being a direct effect of the stimulus. The Python/Matlab implementation for all presented algorithms is available on <;uri xlink:type="simple"> https://github.com/sweichwald/MERLiN<;/uri>.

Type: Article
Title: MERLiN: Mixture Effect Recovery in Linear Networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/JSTSP.2016.2601144
Publisher version: https://doi.org/10.1109/JSTSP.2016.2601144
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: linear mixtures, Causal inference, causal variable construction
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/1496377
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