Cargando…

Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction

Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as a real physical notion so as to formulate it from first principles, however, seems to have gone un...

Descripción completa

Detalles Bibliográficos
Autor principal: Liang, X. San
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8228659/
https://www.ncbi.nlm.nih.gov/pubmed/34071323
http://dx.doi.org/10.3390/e23060679
_version_ 1783712793949634560
author Liang, X. San
author_facet Liang, X. San
author_sort Liang, X. San
collection PubMed
description Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as a real physical notion so as to formulate it from first principles, however, seems to have gone unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.
format Online
Article
Text
id pubmed-8228659
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-82286592021-06-26 Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction Liang, X. San Entropy (Basel) Article Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as a real physical notion so as to formulate it from first principles, however, seems to have gone unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely. MDPI 2021-05-28 /pmc/articles/PMC8228659/ /pubmed/34071323 http://dx.doi.org/10.3390/e23060679 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liang, X. San
Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction
title Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction
title_full Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction
title_fullStr Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction
title_full_unstemmed Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction
title_short Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction
title_sort normalized multivariate time series causality analysis and causal graph reconstruction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8228659/
https://www.ncbi.nlm.nih.gov/pubmed/34071323
http://dx.doi.org/10.3390/e23060679
work_keys_str_mv AT liangxsan normalizedmultivariatetimeseriescausalityanalysisandcausalgraphreconstruction