Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks
利用距离秩的信息不平衡推断高维动态过程的因果关系
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,…