Huang Yu, Fu Zuntao, Franzke Christian L.E.
Detecting causality from observational data is a challenging problem. Here, we propose a machine learning based causality approach, Reservoir Computing Causality (RCC), in order to systematically identify causal relationships between variables. We demonstrate that RCC is able to identify the causal direction, coupling delay, and causal chain relations from time series. Compared to a well-known phase space reconstruction based causality method, Extended Convergent Cross Mapping, RCC does not require the estimation of the embedding dimension and delay time. Moreover, RCC has three additional advantages: (i) robustness to noisy time series; (ii) computational efficiency; and (iii) seamless causal inference from high-dimensional data. We also illustrate the power of RCC in identifying remote causal interactions of high-dimensional systems and demonstrate its usability on a real-world example using atmospheric circulation data. Our results suggest that RCC can accurately detect causal relationships in complex systems.
从观测数据中检测因果关系是一个具有挑战性的问题。本文利用蓄水池计算机算法，发展了一种系统性识别变量间因果关系的机器学习方法。该方法能够从时间序列中识别变量间的因果方向、耦合延迟和因果链关系。与格兰杰因果方法相比，该方法能检测非线性动力系统中的因果关系；与收敛交叉映射方法相比，该方法不需要估计吸引子嵌入参数。此外，这种机器学习方法还表现出了三个优点:(1)对观测噪声具有鲁棒性；(2)计算效率高；(3)对高维混沌动力学中的因果关系检测具有较高的准确性。本文在高维混沌系统、大气环流观测数据中检验了它在实际应用中的有效性。
参考文献：
Huang Yu, Fu Zuntao, Franzke Christian L.E. “Detecting causality from time series in a machine learning framework”. Chaos 30(6):063116, 2020.
Figure (a) Diagram of a variant of the two-layer Lorenz 96 system. (b) A simple diagram of the machine learning configuration for causal detection. (c) The causal chain relation of complex system can be reflected in the causality coefficients.
