Causality in Reversed Time Series: Reversed or Conserved?

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F21%3A43920764" target="_blank" >RIV/00023752:_____/21:43920764 - isvavai.cz</a>

Alternative codes found

RIV/67985807:_____/21:00545822 RIV/68407700:21340/21:00354365

Result on the web

<a href="https://www.mdpi.com/1099-4300/23/8/1067" target="_blank" >https://www.mdpi.com/1099-4300/23/8/1067</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/e23081067" target="_blank" >10.3390/e23081067</a>

Result language

angličtina

Original language name

Causality in Reversed Time Series: Reversed or Conserved?

Original language description

The inference of causal relations between observable phenomena is paramount across scientific disciplines; however, the means for such enterprise without experimental manipulation are limited. A commonly applied principle is that of the cause preceding and predicting the effect, taking into account other circumstances. Intuitively, when the temporal order of events is reverted, one would expect the cause and effect to apparently switch roles. This was previously demonstrated in bivariate linear systems and used in design of improved causal inference scores, while such behaviour in linear systems has been put in contrast with nonlinear chaotic systems where the inferred causal direction appears unchanged under time reversal. The presented work explores the conditions under which the causal reversal happens-either perfectly, approximately, or not at all-using theoretical analysis, low-dimensional examples, and network simulations, focusing on the simplified yet illustrative linear vector autoregressive process of order one. We start with a theoretical analysis that demonstrates that a perfect coupling reversal under time reversal occurs only under very specific conditions, followed up by constructing low-dimensional examples where indeed the dominant causal direction is even conserved rather than reversed. Finally, simulations of random as well as realistically motivated network coupling patterns from brain and climate show that level of coupling reversal and conservation can be well predicted by asymmetry and anormality indices introduced based on the theoretical analysis of the problem. The consequences for causal inference are discussed.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

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

2021

Confidentiality

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

Name of the periodical

Entropy

ISSN

1099-4300

e-ISSN

—

Volume of the periodical

23

Issue of the periodical within the volume

8

Country of publishing house

CH - SWITZERLAND

Number of pages

22

Pages from-to

"Article Number: 1067"

UT code for WoS article

000689122000001

EID of the result in the Scopus database

2-s2.0-85113408849