Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy

‘Causal’ direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability...

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Main Authors:	Abdul Razak, Fatimah, Jensen, Henrik Jeldtoft
Format:	Online
Language:	English
Published:	Public Library of Science 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4067287/

id	pubmed-4067287
recordtype	oai_dc
spelling	pubmed-40672872014-06-25 Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy Abdul Razak, Fatimah Jensen, Henrik Jeldtoft Research Article ‘Causal’ direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of ‘causal’ direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets. Public Library of Science 2014-06-23 /pmc/articles/PMC4067287/ /pubmed/24955766 http://dx.doi.org/10.1371/journal.pone.0099462 Text en © 2014 Abdul Razak, Jensen http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Abdul Razak, Fatimah Jensen, Henrik Jeldtoft
spellingShingle	Abdul Razak, Fatimah Jensen, Henrik Jeldtoft Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy
author_facet	Abdul Razak, Fatimah Jensen, Henrik Jeldtoft
author_sort	Abdul Razak, Fatimah
title	Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy
title_short	Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy
title_full	Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy
title_fullStr	Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy
title_full_unstemmed	Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy
title_sort	quantifying ‘causality’ in complex systems: understanding transfer entropy
description	‘Causal’ direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of ‘causal’ direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.
publisher	Public Library of Science
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4067287/
_version_	1612104883439140864

Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy

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