Causation entropy identifies indirect influences, dominance of neighbors and anticipatory couplings
By Jie Sun, Erik M. Bollt. 2014
184 citations recorded by the source corpus.
Abstract Inference of causality is central in nonlinear time series analysis and science in general. A popular approach to infer causality between two processes is to measure the information flow between them in terms of transfer entropy. Using dynamics of coupled oscillator networks, we show that although transfer entropy can successfully detect information flow in two processes, it often results in erroneous identification of network connections under the presence of indirect interactions, dominance of neighbors, or anticipatory couplings. Such effects are found to be profound for time-dependent networks. To overcome these limitations, we develop a measure called causation entropy and show that its application can lead to reliable identification of true couplings.
Subfields: Cognitive Neuroscience, Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/6fcaba22c15e0df6b14acb557f8f7c6117a63a95
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
In Computer Networks and Communications
In Statistical and Nonlinear Physics
Citations in the corpus
- Causal network reconstruction from time series: From theoretical assumptions to practical estimation. (2018, methodology): “Sun and Bollt (2014) and Sun et al. (2015) developed a discovery algorithm based on the information-theoretic optimal causation entropy principle (algorithm abbreviated as OCE) which reconstructs the lagged parents of a variable X jt by an iterative procedure alleviating the curse of…”