A controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systems
Transfer entropy is emerging as the statistical approach of choice to support the inference of causal interactions in complex systems from time-series of their individual units. With reference to a simple dyadic system composed of two coupled units, the successful application of net transfer entropy...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | Journal of Physics: Complexity |
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| Online Access: | https://doi.org/10.1088/2632-072X/acde2d |
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| author | Rishita Das Maurizio Porfiri |
| author_facet | Rishita Das Maurizio Porfiri |
| author_sort | Rishita Das |
| collection | DOAJ |
| description | Transfer entropy is emerging as the statistical approach of choice to support the inference of causal interactions in complex systems from time-series of their individual units. With reference to a simple dyadic system composed of two coupled units, the successful application of net transfer entropy-based inference relies on unidirectional coupling between the units and their homogeneous dynamics. What happens when the units are bidirectionally coupled and have different dynamics? Through analytical and numerical insights, we show that net transfer entropy may lead to erroneous inference of the dominant direction of influence that stems from its dependence on the units’ individual dynamics. To control for these confounding effects, one should incorporate further knowledge about the units’ time-histories through the recent framework offered by momentary information transfer. In this realm, we demonstrate the use of two measures: controlled and fully controlled transfer entropies, which consistently yield the correct direction of dominant coupling irrespective of the sources and targets individual dynamics. Through the study of two real-world examples, we identify critical limitations with respect to the use of net transfer entropy in the inference of causal mechanisms that warrant prudence by the community. |
| format | Article |
| id | doaj-art-f884b7b65ea54aebb73a41a26d25fc4e |
| institution | DOAJ |
| issn | 2632-072X |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Journal of Physics: Complexity |
| spelling | doaj-art-f884b7b65ea54aebb73a41a26d25fc4e2025-08-20T02:50:01ZengIOP PublishingJournal of Physics: Complexity2632-072X2023-01-014202502010.1088/2632-072X/acde2dA controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systemsRishita Das0https://orcid.org/0000-0001-9785-5109Maurizio Porfiri1https://orcid.org/0000-0002-1480-3539Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University , Brooklyn, NY 11201, United States of America; Center for Urban Science and Progress, New York University , Brooklyn, NY 11201, United States of AmericaDepartment of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University , Brooklyn, NY 11201, United States of America; Center for Urban Science and Progress, New York University , Brooklyn, NY 11201, United States of America; Department of Biomedical Engineering, Tandon School of Engineering, New York University , Brooklyn, NY 11201, United States of AmericaTransfer entropy is emerging as the statistical approach of choice to support the inference of causal interactions in complex systems from time-series of their individual units. With reference to a simple dyadic system composed of two coupled units, the successful application of net transfer entropy-based inference relies on unidirectional coupling between the units and their homogeneous dynamics. What happens when the units are bidirectionally coupled and have different dynamics? Through analytical and numerical insights, we show that net transfer entropy may lead to erroneous inference of the dominant direction of influence that stems from its dependence on the units’ individual dynamics. To control for these confounding effects, one should incorporate further knowledge about the units’ time-histories through the recent framework offered by momentary information transfer. In this realm, we demonstrate the use of two measures: controlled and fully controlled transfer entropies, which consistently yield the correct direction of dominant coupling irrespective of the sources and targets individual dynamics. Through the study of two real-world examples, we identify critical limitations with respect to the use of net transfer entropy in the inference of causal mechanisms that warrant prudence by the community.https://doi.org/10.1088/2632-072X/acde2dcausal analysisinformation theorytransfer entropyphysiologycollective behaviortime-series analysis |
| spellingShingle | Rishita Das Maurizio Porfiri A controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systems Journal of Physics: Complexity causal analysis information theory transfer entropy physiology collective behavior time-series analysis |
| title | A controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systems |
| title_full | A controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systems |
| title_fullStr | A controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systems |
| title_full_unstemmed | A controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systems |
| title_short | A controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systems |
| title_sort | controlled transfer entropy approach to detect asymmetric interactions in heterogeneous systems |
| topic | causal analysis information theory transfer entropy physiology collective behavior time-series analysis |
| url | https://doi.org/10.1088/2632-072X/acde2d |
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