Fast and effective pseudo transfer entropy for bivariate data-driven causal inference

Abstract Identifying, from time series analysis, reliable indicators of causal relationships is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years many methods for data-driven causa...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Riccardo Silini, Cristina Masoller
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e163728f51ca4d9588d462cb776638de
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e163728f51ca4d9588d462cb776638de
record_format	dspace
spelling	oai:doaj.org-article:e163728f51ca4d9588d462cb776638de2021-12-02T17:32:59ZFast and effective pseudo transfer entropy for bivariate data-driven causal inference10.1038/s41598-021-87818-32045-2322https://doaj.org/article/e163728f51ca4d9588d462cb776638de2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87818-3https://doaj.org/toc/2045-2322Abstract Identifying, from time series analysis, reliable indicators of causal relationships is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years many methods for data-driven causal inference have been proposed; however, their success largely depends on the characteristics of the system under investigation. Often, their data requirements, computational cost or number of parameters limit their applicability. Here we propose a computationally efficient measure for causality testing, which we refer to as pseudo transfer entropy (pTE), that we derive from the standard definition of transfer entropy (TE) by using a Gaussian approximation. We demonstrate the power of the pTE measure on simulated and on real-world data. In all cases we find that pTE returns results that are very similar to those returned by Granger causality (GC). Importantly, for short time series, pTE combined with time-shifted (T-S) surrogates for significance testing strongly reduces the computational cost with respect to the widely used iterative amplitude adjusted Fourier transform (IAAFT) surrogate testing. For example, for time series of 100 data points, pTE and T-S reduce the computational time by $$82\%$$ 82 % with respect to GC and IAAFT. We also show that pTE is robust against observational noise. Therefore, we argue that the causal inference approach proposed here will be extremely valuable when causality networks need to be inferred from the analysis of a large number of short time series.Riccardo SiliniCristina MasollerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Riccardo Silini Cristina Masoller Fast and effective pseudo transfer entropy for bivariate data-driven causal inference
description	Abstract Identifying, from time series analysis, reliable indicators of causal relationships is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years many methods for data-driven causal inference have been proposed; however, their success largely depends on the characteristics of the system under investigation. Often, their data requirements, computational cost or number of parameters limit their applicability. Here we propose a computationally efficient measure for causality testing, which we refer to as pseudo transfer entropy (pTE), that we derive from the standard definition of transfer entropy (TE) by using a Gaussian approximation. We demonstrate the power of the pTE measure on simulated and on real-world data. In all cases we find that pTE returns results that are very similar to those returned by Granger causality (GC). Importantly, for short time series, pTE combined with time-shifted (T-S) surrogates for significance testing strongly reduces the computational cost with respect to the widely used iterative amplitude adjusted Fourier transform (IAAFT) surrogate testing. For example, for time series of 100 data points, pTE and T-S reduce the computational time by $$82\%$$ 82 % with respect to GC and IAAFT. We also show that pTE is robust against observational noise. Therefore, we argue that the causal inference approach proposed here will be extremely valuable when causality networks need to be inferred from the analysis of a large number of short time series.
format	article
author	Riccardo Silini Cristina Masoller
author_facet	Riccardo Silini Cristina Masoller
author_sort	Riccardo Silini
title	Fast and effective pseudo transfer entropy for bivariate data-driven causal inference
title_short	Fast and effective pseudo transfer entropy for bivariate data-driven causal inference
title_full	Fast and effective pseudo transfer entropy for bivariate data-driven causal inference
title_fullStr	Fast and effective pseudo transfer entropy for bivariate data-driven causal inference
title_full_unstemmed	Fast and effective pseudo transfer entropy for bivariate data-driven causal inference
title_sort	fast and effective pseudo transfer entropy for bivariate data-driven causal inference
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e163728f51ca4d9588d462cb776638de
work_keys_str_mv	AT riccardosilini fastandeffectivepseudotransferentropyforbivariatedatadrivencausalinference AT cristinamasoller fastandeffectivepseudotransferentropyforbivariatedatadrivencausalinference
_version_	1718380155348451328

Fast and effective pseudo transfer entropy for bivariate data-driven causal inference

Ejemplares similares