Building Proven Causal Model Bases for Strategic Decision Support

Since many Decision Support Systems (DSS) in the area of causal strategy planning methods incorporate techniques to draw conclusions from an underlying model but fail to prove the implicitly assumed hypotheses within the latter, this paper focuses on the improvement of the model base quality. Therefore, this approach employs Artificial Neural Networks (ANNs) to infer the underlying causal functions from empirical time series. As a prerequisite for this, an automated proof of causality for nomothetic cause-and-effect hypotheses has to be developed.