A new method of nonlinear causality detection: Reservoir computing Granger causality

Identifying causal and interaction relationships from observational time series is a key step toward understanding complex systems and is also a challenging problem. Many methods have been developed to detect and identify the possible causal link between two variables. However, most of them often suffer from the false detection problems, including false-positive (detected causal links do not exist) and false-negative (an existing causal link fails to be detected). Compared to false-negative problem, false-positive is a more serious problem found in nonlinear systems or processes for almost all causality detection methods. In this study, a new method combining the advantages of Reservoir Computing (RC, a machine learning method) and classical Granger causality detection was developed to fully solve false-positive problems or considerably lower the false-positive rate in detecting causal links of nonlinear systems. Three typical coupled systems with known causal links are applied to show the better performance of new Reservoir Computing Granger (RCG) method over traditional nonlinear Granger causality and its extension methods, which indicates that RCG can accurately detect causal relationships in complex systems and its great potential in exploring the causal and interaction relationships from observational time series.