Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series.

ABSTRACT: The detection of causal effects among simultaneous observations provides knowledge about the underlying network, and is a topic of interests in many scientific areas. Over the years different causality measures have been developed, each with their own advantages and disadvantages. However, an extensive evaluation study is missing. In this work we consider some of the best-known causality measures i.e., cross-correlation, (conditional) Granger causality index (CGCI), partial directed coherence (PDC), directed transfer function (DTF), and partial mutual information on mixed embedding (PMIME). To correct for noise-related spurious connections, each measure (except PMIME) is tested for statistical significance based on surrogate data. The performance of the causality metrics is evaluated on a

SUBMITTER: Heyse J 

PROVIDER: S-EPMC8569855 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature