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A Semi-Automatic Approach for Confounding-Aware Subgroup Discovery

This paper presents a semi-automatic approach for confounding-aware subgroup discovery: Confounding essentially disturbs the measured effect of an association between variables due to the influence of other parameters that were not considered. The proposed method is embedded into a general subgroup discovery approach, and provides the means for detecting potentially confounded subgroup patterns, other unconfounded relations, and/or patterns that are affected by effect-modification. Since there is no purely automatic test for confounding, the discovered relations are presented to the user in a semi-automatic approach. Furthermore, we utilize (causal) domain knowledge for improving the results of the algorithm, since confounding is itself a causal concept. The applicability and benefit of the presented technique is illustrated by real-world examples from a case-study in the medical domain.