Abstract
This paper presents a causal subgroup analysis approach for the detection of confounding: 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 relations that are potentially confounded by external (confounding) factors. In a semi-automatic approach, the network and the discovered relations are then 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.
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Atzmueller, M., Puppe, F. (2009). Causal Subgroup Analysis for Detecting Confounding. In: Seipel, D., Hanus, M., Wolf, A. (eds) Applications of Declarative Programming and Knowledge Management. INAP WLP 2007 2007. Lecture Notes in Computer Science(), vol 5437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00675-3_9
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DOI: https://doi.org/10.1007/978-3-642-00675-3_9
Publisher Name: Springer, Berlin, Heidelberg
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