TY - JOUR
T1 - Continuity Scaling
T2 - A Rigorous Framework for Detecting and Quantifying Causality Accurately
AU - Ying, Xiong
AU - Leng, Si Yang
AU - Ma, Huan Fei
AU - Nie, Qing
AU - Lai, Ying Cheng
AU - Lin, Wei
N1 - Funding Information:
W.L. is supported by the National Key R&D Program of China (Grant No. 2018YFC0116600), by the National Natural Science Foundation of China (Grant Nos. 11925103 and 61773125), by the STCSM (Grant No. 18DZ1201000), and by the Shanghai Municipal Science and Technology Major Project (No. 2021SHZDZX0103). Y.-C.L. is supported by AFOSR (Grant No. FA9550-21-1-0438). S.-Y.L. is supported by the National Natural Science Foundation of China (No. 12101133) and “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (No. 20CG01). Q.N. is partially supported by NSF (Grant No. DMS1763272) and the Simons Foundation (Grant No. 594598). H.-F.M. is supported by the National Natural Science Foundation of China (Grant No. 12171350) and by the National Key R&D Program of China (Grant No. 2018YFA0801100).
Publisher Copyright:
Copyright © 2022 Xiong Ying et al. Exclusive Licensee Science and Technology Review Publishing House. Distributed under a Creative Commons Attribution License (CC BY 4.0).
PY - 2022
Y1 - 2022
N2 - Data-based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering, and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross-map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.
AB - Data-based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering, and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross-map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.
UR - http://www.scopus.com/inward/record.url?scp=85129637238&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85129637238&partnerID=8YFLogxK
U2 - 10.34133/2022/9870149
DO - 10.34133/2022/9870149
M3 - Article
AN - SCOPUS:85129637238
SN - 2096-5168
VL - 2022
JO - Research
JF - Research
M1 - 9870149
ER -