This paper suggests a new way of comparing and analyzing causal theories. The main contribution is a meta-model that represents causal theories and a taxonomy of inter-theory relationships. The inter-theory relationships can be automatically calculated for two theories that are described with the meta-model. Two visualizations are presented with which to analyze set of theories: the inter-theory relationship matrix and the theory evolution graph. An exemplary application of the approach is shown for a small set of information systems theories. The proposed approach should help researchers improve their understanding of the contribution and evolution of theories.
The number of scientific publications has increased exponentially in the last decades. To better compare these ever-increasing results and to help further replication studies, the use of open data has been suggested. But how should empirical data be described? This paper presents a meta-model for empirical findings that is conceptually linked with the formal description of causal theories. Based on the meta-model, we present methods to calculate the expected empirical outcome for causal theories given a specific research setting. Additionally, a method for automatically determining the relationship between a causal theory and an empirical test is suggested. Based on the meta-model and the methods, a new form of visualization (theory-data maps) is used to show the relationship between causal theories and empirical data. We demonstrate the preliminary applicability of the approach based on an example application to information systems theories and empirical results. Keywords: Causality, Theory, Empirical Data, Conceptual Model, Open Data, Replication