Towards Causal Fuzzy System Rules Using Causal Direction

Generating (fuzzy) rule bases from data can provide a rapid pathway to constructing (fuzzy) systems. However, direct rule generation approaches tend to generate very large numbers of rules. One reason for this is that such techniques are not designed to differentiate between relationships of variables reflecting a causal link and those where such a link reflects a spurious correlation in the data set. In prior work, we discussed how causal discovery techniques, and specifically the subset resulting of variables within the Markov blanket can be leveraged to focus on the generation of rules for variables with a causal link. In this paper, we broaden this discussion, outlining a road-map to explain how causal discovery and its outputs-causal graphs-can be used towards refining (fuzzy) rule generation techniques. As a next step on this road-map, we present an initial approach which leverages the causal direction captured in the graph to further reduce the set of variables from those captured in the Markov blanket. Initial results show that the approach, combined with a traditional fuzzy rule generation technique such as the Wang-Mendel approach, produces competitive performance and concise rule bases-highlighting a path towards improved fuzzy system interpretability.