Higher order definition of causality by optimally conditioned transfer entropy

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00617882" target="_blank" >RIV/67985807:_____/25:00617882 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1103/PhysRevE.111.L042302" target="_blank" >https://doi.org/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>

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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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10102 - Applied mathematics

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Physical Review E

ISSN

1539-3755

e-ISSN

2470-0053

Volume of the periodical

111

Issue of the periodical within the volume

April 2025

Country of publishing house

US - UNITED STATES

Number of pages

6

Pages from-to

L042302

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105003858717