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Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data

Weichwald, S; Gretton, A; Schölkopf, B; Grosse-Wentrup, M; (2016) Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data. In: PRNI 2016: 6th International Workshop on Pattern Recognition in Neuroimaging. Institute of Electrical and Electronic Engineers (IEEE) Green open access

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Abstract

Causal inference concerns the identification of cause-effect relationships between variables. However, often only linear combinations of variables constitute meaningful causal variables. For example, recovering the signal of a cortical source from electroencephalography requires a well-tuned combination of signals recorded at multiple electrodes. We recently introduced the MERLiN (Mixture Effect Recovery in Linear Networks) algorithm that is able to recover, from an observed linear mixture, a causal variable that is a linear effect of another given variable. Here we relax the assumption of this cause-effect relationship being linear and present an extended algorithm that can pick up non-linear cause-effect relationships. Thus, the main contribution is an algorithm (and ready to use code) that has broader applicability and allows for a richer model class. Furthermore, a comparative analysis indicates that the assumption of linear cause-effect relationships is not restrictive in analysing electroencephalographic data.

Type: Proceedings paper
Title: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data
Event: PRNI 2016, 6th International Workshop on Pattern Recognition in Neuroimaging, 22-24 June 2016, Trento, Italy
ISBN-13: 9781467365307
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/PRNI.2016.7552331
Publisher version: https://doi.org/10.1109/PRNI.2016.7552331
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: regression-based conditional independence criterion, causal inference, causal variable construction, instrumental variable, linear mixtures
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/1496376
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