Transfer Zero-Entropy and Its Application for Capturing Cause and Effect Relationship Between Variables


Detection of causality is a crucial and challenging problem in root cause and hazard propagation analysis. It has been shown that the transfer entropy approach may be a terribly useful tool in quantifying directional causal influence for both linear and nonlinear relationships. A key assumption for this technique is that the sampled knowledge ought to follow a well-defined likelihood distribution; however this assumption may not hold for some industrial process information. During this paper, a brand new information theory-based mostly measure, transfer zero-entropy (T0E), is proposed for causality analysis on the basis of the definitions of 0-entropy and 0-info while not assuming a chance house. For the cases of more than 2 variables, an immediate T0E (DT0E) concept is presented to detect whether or not there's a direct data and/or material flow pathway from one variable to another. Estimation strategies for the T0E and the DT0E are addressed. The effectiveness of the proposed methodology is illustrated by 2 knowledge sets, one primarily based on data from a pilot scale method and a second analysis based on data from a benchmark industrial case study.

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