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TraCE: Trajectory Counterfactual Explanation Scores (2309.15965v2)

Published 27 Sep 2023 in cs.LG, cs.CY, and math.MG

Abstract: Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box classifier. In this paper, we propose to extend the use of counterfactuals to evaluate progress in sequential decision making tasks. To this end, we introduce a model-agnostic modular framework, TraCE (Trajectory Counterfactual Explanation) scores, which is able to distill and condense progress in highly complex scenarios into a single value. We demonstrate TraCE's utility across domains by showcasing its main properties in two case studies spanning healthcare and climate change.

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Authors (8)
  1. Jeffrey N. Clark (7 papers)
  2. Edward A. Small (4 papers)
  3. Nawid Keshtmand (6 papers)
  4. Michelle W. L. Wan (2 papers)
  5. Elena Fillola Mayoral (2 papers)
  6. Enrico Werner (3 papers)
  7. Christopher P. Bourdeaux (3 papers)
  8. Raul Santos-Rodriguez (70 papers)
Citations (1)
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