Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.
Science. 2012 Oct 26;338(6106):496-500. doi: 10.1126/science.1227079. Epub 2012 Sep 20.
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.
识别因果网络对于制定有效的政策和管理建议至关重要，这些建议涉及气候、流行病学、金融监管等诸多领域。我们引入了一种基于非线性状态空间重构的方法，可以区分因果关系和相关性。该方法扩展到不可分离的弱连接动态系统（当前格兰杰因果关系范式未涵盖的情况）。该方法通过简单的模型进行了说明（与现实世界不同，我们知道基本方程/关系，因此可以检查我们方法的有效性），并应用于真实的生态系统，包括有争议的沙丁鱼-凤尾鱼-温度问题。
