Journal
SCIENCE
Volume 338, Issue 6106, Pages 496-500Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.1227079
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Funding
- NSF [DEB-1020372]
- NSF/NOAA CAMEO Award [NA08OAR4320894]
- McQuown Chair in Natural Science
- Sugihara Family Trust
- National Taiwan University
- National Science Council of Taiwan
- NSF graduate research fellowships
- EPA STAR graduate fellowship
- Division Of Environmental Biology
- Direct For Biological Sciences [1020372] Funding Source: National Science Foundation
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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.
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