Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks

• 成果类型: Article
• 署名作者: Del Tatto, Vittorio; Fortunato, Gianfranco; Bueti, Domenica; Laio, Alessandro
• 署名单位: International School for Advanced Studies (SISSA); Abdus Salam International Centre for Theoretical Physics (ICTP)
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11623
• DOI: 10.1073/pnas.2317256121
• 发表日期: 2024-05-07
• 关键词: speed

摘要

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high -dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model -free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.