TraCE: Trajectory Counterfactual Explanation Scores
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917575044104192 |
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| author | Clark, Jeffrey N. Small, Edward A. Keshtmand, Nawid Wan, Michelle W. L. Mayoral, Elena Fillola Werner, Enrico Bourdeaux, Christopher P. Santos-Rodriguez, Raul |
| author_facet | Clark, Jeffrey N. Small, Edward A. Keshtmand, Nawid Wan, Michelle W. L. Mayoral, Elena Fillola Werner, Enrico Bourdeaux, Christopher P. Santos-Rodriguez, Raul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_15965 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | TraCE: Trajectory Counterfactual Explanation Scores Clark, Jeffrey N. Small, Edward A. Keshtmand, Nawid Wan, Michelle W. L. Mayoral, Elena Fillola Werner, Enrico Bourdeaux, Christopher P. Santos-Rodriguez, Raul Machine Learning Computers and Society Metric Geometry 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. |
| title | TraCE: Trajectory Counterfactual Explanation Scores |
| topic | Machine Learning Computers and Society Metric Geometry |
| url | https://arxiv.org/abs/2309.15965 |