Detecting dynamical causality by intersection cardinal concavity.

ABSTRACT: Discovering causality from observed time series data is of great importance in various disciplines but also a challenging task. In recent years, cross-mapping methods have been developed to solve the non-separability or false-negative problem that traditional methods, e.g., Granger causality or transfer entropy, cannot handle. However, these cross-mapping methods suffer still from nonlinearity and robustness problems on the noisy data. Here, we propose cross-mapping cardinality (CMC), which detects direct causality in a robust and nonlinear manner by quantifying the intersectional cardinality (IC) from the neighbors of the cause variable to the cross-mapping neighbors of the effect variable in the delay embedding space. We theoretically and computationally show the new causal concept "IC c

SUBMITTER: Tao P 

PROVIDER: S-EPMC12744533 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature