Publication Date:
2012-09-22
Description:
Identifying causal networks is important for effective policy and management recommendations on climate, epidemiology, financial regulation, and much else. We introduce a method, based on nonlinear state space reconstruction, that can distinguish causality from correlation. It extends to nonseparable weakly connected dynamic systems (cases not covered by the current Granger causality paradigm). The approach is illustrated both by simple models (where, in contrast to the real world, we know the underlying equations/relations and so can check the validity of our method) and by application to real ecological systems, including the controversial sardine-anchovy-temperature problem.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Sugihara, George -- May, Robert -- Ye, Hao -- Hsieh, Chih-hao -- Deyle, Ethan -- Fogarty, Michael -- Munch, Stephan -- New York, N.Y. -- Science. 2012 Oct 26;338(6106):496-500. doi: 10.1126/science.1227079. Epub 2012 Sep 20.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. gsugihara@ucsd.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/22997134" target="_blank"〉PubMed〈/a〉
Keywords:
*Causality
;
Ciliophora
;
*Ecosystem
;
*Models, Statistical
;
Nonlinear Dynamics
;
Paramecium
Print ISSN:
0036-8075
Electronic ISSN:
1095-9203
Topics:
Biology
,
Chemistry and Pharmacology
,
Computer Science
,
Medicine
,
Natural Sciences in General
,
Physics
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