Causal Subgroup Analysis for Detecting Confounding

  • Martin Atzmueller
  • Frank Puppe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5437)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Martin Atzmueller
    • 1
  • Frank Puppe
    • 1
  1. 1.Department of Computer Science VIUniversity of WürzburgWürzburgGermany

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