TraCE: Trajectory Counterfactual Explanation Scores

• URL: http://arxiv.org/abs/2309.15965v2
• Date: Fri, 26 Jan 2024 07:38:41 GMT
• Title: TraCE: Trajectory Counterfactual Explanation Scores
• Authors: Jeffrey N. Clark, Edward A. Small, Nawid Keshtmand, Michelle W.L. Wan, Elena Fillola Mayoral, Enrico Werner, Christopher P. Bourdeaux, Raul Santos-Rodriguez
• Abstract summary: We propose to extend the use of counterfactuals to evaluate progress in sequential decision making tasks. We introduce a model-agnostic modular framework, TraCE, which is able to distill and condense progress in highly complex scenarios into a single value.
• Score: 1.149801527015106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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