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Mining Exceptional Mediation Models

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Foundations of Intelligent Systems (ISMIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12117))

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Abstract

In statistics, mediation models aim to identify and explain the direct and indirect effects of an independent variable on a dependent variable. In heterogeneous data, the observed effects might vary for parts of the data. In this paper, we develop an approach for identifying interpretable data subgroups that induce exceptionally different effects in a mediation model. For that purpose, we introduce mediation models as a novel model class for the exceptional model mining framework, introduce suitable interestingness measures for several subtasks, and demonstrate the benefits of our approach on synthetic and empirical datasets.

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Notes

  1. 1.

    Code is available at http://florian.lemmerich.net/mediation-emm.

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Acknowledgement

Funded by the Excellence Initiative of the German federal and state governments.

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Authors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Florian Lemmerich, Christoph Kiefer, Benedikt Langenberg, Jeffry Cacho Aboukhalil & Axel Mayer

Authors
  1. Florian Lemmerich
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  2. Christoph Kiefer
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  3. Benedikt Langenberg
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  4. Jeffry Cacho Aboukhalil
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  5. Axel Mayer
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Corresponding author

Correspondence to Florian Lemmerich .

Editor information

Editors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Denis Helic

  2. University of Klagenfurt, Klagenfurt, Austria

    Gerhard Leitner

  3. Graz University of Technology, Graz, Austria

    Martin Stettinger

  4. Graz University of Technology, Graz, Austria

    Alexander Felfernig

  5. University of North Carolina at Charlotte, Charlotte, NC, USA

    Zbigniew W. Raś

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Lemmerich, F., Kiefer, C., Langenberg, B., Cacho Aboukhalil, J., Mayer, A. (2020). Mining Exceptional Mediation Models. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_30

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  • DOI: https://doi.org/10.1007/978-3-030-59491-6_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59490-9

  • Online ISBN: 978-3-030-59491-6

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