Variable Selection for Optimal Decision Making

  • Authors:

  • Lacey Gunter;Ji Zhu;Susan Murphy

  • Affiliations:

  • Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA and Institute for Social Research, University of Michigan, Ann Arbor, MI 48109, USA;Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA;Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA and Institute for Social Research, University of Michigan, Ann Arbor, MI 48109, USA

  • Venue:
  • AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
  • Year:
  • 2007

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Abstract

This paper discusses variable selection for medical decision making; in particular decisions regarding which treatment to provide a patient. Current variable selection methods were designed for use in prediction applications. These techniques often leave behind small but important interaction variables that are critical when the goal is decision making rather than prediction. This paper presents a new method designed to find variables that aid in decision making and demonstrates the method on data from a clinical trial for treatment of depression.