Detection of direct causality based on process data

Direct causality detection is an important and challenging problem in root cause and hazard propagation analysis. Several methods provide effective solutions to this problem for linear relationships. For nonlinear situations, currently only causality analysis can be conducted, but the direct causality cannot be identified based on process data. In this paper, we describe a direct causality detection approach suitable for both linear and nonlinear connections. Based on an extension of the transfer entropy approach, a direct transfer entropy (DTE) concept is proposed to detect whether there is a direct information and/or material flow pathway from one variable to another. A discrete DTE and a differential DTE are defined for discrete and continuous random variables, respectively; and the relationship between them is discussed. The effectiveness of the proposed method is illustrated by two examples and an experimental case study.