A Knowledge-Intensive Approach for Semi-automatic Causal Subgroup Discovery

This paper presents a methodological view on knowledge-intensive causal subgroup discovery implemented in a semi-automatic approach. We show how to identify causal relations between subgroups by generating an extended causal subgroup network utilizing background knowledge. Using the links within the network we can identify causal relations, but also relations that are potentially confounded and/or effect-modified by external (confounding) factors. In a semi-automatic approach, the network and the discovered relations are presented to the user as an intuitive visualization. The applicability and benefit of the presented technique is illustrated by examples from a case-study in the medical domain.