Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2022
- Source ID
- 10.34133/2022/9870149
Entities
People
- Huanfei Ma
- Qing Nie
- Siyang Leng
- Wei Lin
- Ying Xiong
- Ying-Cheng Lai
Organizations
- Air Force Office of Scientific Research
- Arizona State University
- Fudan University
- National Natural Science Foundation of China
- National Science Foundation
- Shanghai Education Development Foundation
- Shanghai Municipal Science and Technology Commission
- Simons Foundation
- Soochow University
- University of California, Irvine