TRR 181 Seminar "Detecting causality from time series in a machine learning framework" by Prof. Dr. Christian L. E. Franzke (Pusan National University)
The TRR 181 seminar is held every other week in the semester and as announced during semester break. The locations of the seminar changes between the three TRR181 locations, but is broadcastet online for all members of the TRR.
The TRR 181 seminar is held by Prof. Dr. Christian L. E. Franzke (Pusan National University) on July 14, 11 am.
Detecting causality from time series in a machine learning framework
Abstract
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.