Higher order definition of causality by optimally conditioned transfer entropy
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F25%3A43921521" target="_blank" >RIV/00023752:_____/25:43921521 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/25:00390507
Result on the web
<a href="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.111.L042302" target="_blank" >https://journals.aps.org/pre/abstract/10.1103/PhysRevE.111.L042302</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1103/PhysRevE.111.L042302" target="_blank" >10.1103/PhysRevE.111.L042302</a>
Alternative languages
Result language
angličtina
Original language name
Higher order definition of causality by optimally conditioned transfer entropy
Original language description
The description of the dynamics of complex systems, in particular the capture of the interaction structure and causal relationships between elements of the system, is one of the central questions of interdisciplinary research. While the characterization of pairwise causal interactions is a relatively ripe field with established theoretical concepts and the current focus is on technical issues of their efficient estimation, it turns out that the standard concepts such as Granger causality or transfer entropy may not faithfully reflect possible synergies or interactions of higher orders, phenomena highly relevant for many real-world complex systems. In this paper, we propose a generalization and refinement of the information-theoretic approach to causal inference, enabling the description of truly multivariate, rather than multiple pairwise, causal interactions, and moving thus from causal networks to causal hypernetworks. In particular, while keeping the ability to control for mediating variables or common causes, in case of purely synergistic interactions such as the exclusive disjunction, it ascribes the causal role to the multivariate causal set but not to individual inputs, distinguishing it thus from the case of, e.g., two additive univariate causes. We demonstrate this concept by application to illustrative theoretical examples as well as a biophysically realistic simulation of biological neuronal dynamics recently reported to employ synergistic computations.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EH22_008%2F0004643" target="_blank" >EH22_008/0004643: Brain dynamics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Physical Review E
ISSN
2470-0045
e-ISSN
2470-0053
Volume of the periodical
111
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
6
Pages from-to
"Article Number L042302"
UT code for WoS article
001481089300002
EID of the result in the Scopus database
2-s2.0-105003858717