This paper presents a semi-automatic approach for confounding-aware subgroup discovery: We present a method that provides the means for detecting poten- tially 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 subsequently pre- sented to the user in a semi-automatic approach. Further- more, we show how to utilize (causal) domain knowledge for improving the results of the algorithm, since confound- ing is itself a causal concept. The applicability and benefit of the presented technique is illustrated by examples from a case-study in the medical domain.