Ye Hao, Deyle Ethan R, Gilarranz Luis J, Sugihara George
Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
Integrative Ecology Group, Estación Biológica de Doñana, CSIC, Sevilla, Spain.
Sci Rep. 2015 Oct 5;5:14750. doi: 10.1038/srep14750.
An important problem across many scientific fields is the identification of causal effects from observational data alone. Recent methods (convergent cross mapping, CCM) have made substantial progress on this problem by applying the idea of nonlinear attractor reconstruction to time series data. Here, we expand upon the technique of CCM by explicitly considering time lags. Applying this extended method to representative examples (model simulations, a laboratory predator-prey experiment, temperature and greenhouse gas reconstructions from the Vostok ice core, and long-term ecological time series collected in the Southern California Bight), we demonstrate the ability to identify different time-delayed interactions, distinguish between synchrony induced by strong unidirectional-forcing and true bidirectional causality, and resolve transitive causal chains.
许多科学领域面临的一个重要问题是仅从观测数据中识别因果效应。近期的方法（收敛交叉映射，CCM）通过将非线性吸引子重构的思想应用于时间序列数据，在这个问题上取得了重大进展。在这里，我们通过明确考虑时间滞后，对CCM技术进行了扩展。将这种扩展方法应用于代表性实例（模型模拟、实验室捕食者 - 猎物实验、从沃斯托克冰芯重建温度和温室气体，以及在南加利福尼亚湾收集的长期生态时间序列），我们展示了识别不同时间延迟相互作用的能力，区分由强单向强迫引起的同步和真正的双向因果关系，以及解析传递因果链的能力。
