Reliability of the Granger causality inference
How to characterize information flows in physical, biological, and social systems remains a major theoretical challenge. Granger causality (GC) analysis has been widely used to investigate information flow through causal interactions. We address one of the central questions in GC analysis, that is,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2014-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/16/4/043016 |
| _version_ | 1827874270842191872 |
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| author | Douglas Zhou Yaoyu Zhang Yanyang Xiao David Cai |
| author_facet | Douglas Zhou Yaoyu Zhang Yanyang Xiao David Cai |
| author_sort | Douglas Zhou |
| collection | DOAJ |
| description | How to characterize information flows in physical, biological, and social systems remains a major theoretical challenge. Granger causality (GC) analysis has been widely used to investigate information flow through causal interactions. We address one of the central questions in GC analysis, that is, the reliability of the GC evaluation and its implications for the causal structures extracted by this analysis. Our work reveals that the manner in which a continuous dynamical process is projected or coarse-grained to a discrete process has a profound impact on the reliability of the GC inference, and different sampling may potentially yield completely opposite inferences. This inference hazard is present for both linear and nonlinear processes. We emphasize that there is a hazard of reaching incorrect conclusions about network topologies, even including statistical (such as small-world or scale-free) properties of the networks, when GC analysis is blindly applied to infer the network topology. We demonstrate this using a small-world network for which a drastic loss of small-world attributes occurs in the reconstructed network using the standard GC approach. We further show how to resolve the paradox that the GC analysis seemingly becomes less reliable when more information is incorporated using finer and finer sampling. Finally, we present strategies to overcome these inference artifacts in order to obtain a reliable GC result. |
| first_indexed | 2024-03-12T16:49:52Z |
| format | Article |
| id | doaj.art-b20d92f342dc46a7bb72eeb279c826a5 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:49:52Z |
| publishDate | 2014-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-b20d92f342dc46a7bb72eeb279c826a52023-08-08T11:24:43ZengIOP PublishingNew Journal of Physics1367-26302014-01-0116404301610.1088/1367-2630/16/4/043016Reliability of the Granger causality inferenceDouglas Zhou0Yaoyu Zhang1Yanyang Xiao2David Cai3Department of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University , Shanghai 200240, People's Republic of ChinaDepartment of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University , Shanghai 200240, People's Republic of ChinaDepartment of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University , Shanghai 200240, People's Republic of ChinaDepartment of Mathematics, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University , Shanghai 200240, People's Republic of China; Courant Institute of Mathematical Sciences & Center for Neural Science, New York University , New York, NY 10012, USA; NYUAD Institute, New York University Abu Dhabi , PO Box 129188, Abu Dhabi, UAEHow to characterize information flows in physical, biological, and social systems remains a major theoretical challenge. Granger causality (GC) analysis has been widely used to investigate information flow through causal interactions. We address one of the central questions in GC analysis, that is, the reliability of the GC evaluation and its implications for the causal structures extracted by this analysis. Our work reveals that the manner in which a continuous dynamical process is projected or coarse-grained to a discrete process has a profound impact on the reliability of the GC inference, and different sampling may potentially yield completely opposite inferences. This inference hazard is present for both linear and nonlinear processes. We emphasize that there is a hazard of reaching incorrect conclusions about network topologies, even including statistical (such as small-world or scale-free) properties of the networks, when GC analysis is blindly applied to infer the network topology. We demonstrate this using a small-world network for which a drastic loss of small-world attributes occurs in the reconstructed network using the standard GC approach. We further show how to resolve the paradox that the GC analysis seemingly becomes less reliable when more information is incorporated using finer and finer sampling. Finally, we present strategies to overcome these inference artifacts in order to obtain a reliable GC result.https://doi.org/10.1088/1367-2630/16/4/043016causal information flowreliability of causal inferencesampling hazardssmall-world topology05.45.Tp02.50.Tt |
| spellingShingle | Douglas Zhou Yaoyu Zhang Yanyang Xiao David Cai Reliability of the Granger causality inference New Journal of Physics causal information flow reliability of causal inference sampling hazards small-world topology 05.45.Tp 02.50.Tt |
| title | Reliability of the Granger causality inference |
| title_full | Reliability of the Granger causality inference |
| title_fullStr | Reliability of the Granger causality inference |
| title_full_unstemmed | Reliability of the Granger causality inference |
| title_short | Reliability of the Granger causality inference |
| title_sort | reliability of the granger causality inference |
| topic | causal information flow reliability of causal inference sampling hazards small-world topology 05.45.Tp 02.50.Tt |
| url | https://doi.org/10.1088/1367-2630/16/4/043016 |
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