Extended-AUDI method for simultaneous determination of causality and models from process data

To the best of our knowledge, there are few methods which can determine both causality and models from process data, although both of them are crucial in practical applications. The extended augmented UD identification (EAUDI) is an identification approach which does not need a priori causal relationship between variables in advance. In this method, however, the information contained in the augmented information matrix (AIM) is still not fully utilized and yet helpful for causality analysis, namely, whether the values of cross-regressive coefficients are sufficiently weak to be considered as insignificant. Based on this, the EAUDI method is further extended to detect causality from process data, and it can also provide models of all connecting paths simultaneously. Moreover, hypothesis testing (F-distribution) is proposed to verify the results of this approach (by testing cross-regressive coefficients). The effectiveness of the proposed method is demonstrated by numerical examples.