Higher order definition of causality by optimally conditioned transfer entropy

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21340/25:00390507

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Higher order definition of causality by optimally conditioned transfer entropy

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Higher order definition of causality by optimally conditioned transfer entropy

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EH22_008%2F0004643" target="_blank" >EH22_008/0004643: Dynamika mozku</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Physical Review E

ISSN

2470-0045

e-ISSN

2470-0053

Svazek periodika

111

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

6

Strana od-do

"Article Number L042302"

Kód UT WoS článku

001481089300002

EID výsledku v databázi Scopus

2-s2.0-105003858717