Fu Zuntao
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.
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.
