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arXiv · 2309.15965

TraCE: Trajectory Counterfactual Explanation Scores

Jeffrey N. Clark·Edward A. Small·Nawid Keshtmand·Michelle W. L. Wan·Elena Fillola Mayoral·Enrico Werner·Christopher P. Bourdeaux·Raul Santos-Rodriguez
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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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Jeffrey N. Clark, Edward A. Small, Nawid Keshtmand, Michelle W. L. Wan, Elena Fillola Mayoral, Enrico Werner, Christopher P. Bourdeaux, Raul Santos-Rodriguez. 2023-09-27. TraCE: Trajectory Counterfactual Explanation Scores. https://arxiv.org/abs/2309.15965

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