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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2410.14522 |
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| _version_ | 1866912076221382656 |
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| author | Williams, Joshua Nathaniel Katakkar, Anurag Heidari, Hoda Kolter, J. Zico |
| author_facet | Williams, Joshua Nathaniel Katakkar, Anurag Heidari, Hoda Kolter, J. Zico |
| contents | Counterfactual explanations have been a popular method of post-hoc explainability for a variety of settings in Machine Learning. Such methods focus on explaining classifiers by generating new data points that are similar to a given reference, while receiving a more desirable prediction. In this work, we investigate a framing for counterfactual generation methods that considers counterfactuals not as independent draws from a region around the reference, but as jointly sampled with the reference from the underlying data distribution. Through this framing, we derive a distance metric, tailored for counterfactual similarity that can be applied to a broad range of settings. Through both quantitative and qualitative analyses of counterfactual generation methods, we show that this framing allows us to express more nuanced dependencies among the covariates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14522 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rethinking Distance Metrics for Counterfactual Explainability Williams, Joshua Nathaniel Katakkar, Anurag Heidari, Hoda Kolter, J. Zico Machine Learning Counterfactual explanations have been a popular method of post-hoc explainability for a variety of settings in Machine Learning. Such methods focus on explaining classifiers by generating new data points that are similar to a given reference, while receiving a more desirable prediction. In this work, we investigate a framing for counterfactual generation methods that considers counterfactuals not as independent draws from a region around the reference, but as jointly sampled with the reference from the underlying data distribution. Through this framing, we derive a distance metric, tailored for counterfactual similarity that can be applied to a broad range of settings. Through both quantitative and qualitative analyses of counterfactual generation methods, we show that this framing allows us to express more nuanced dependencies among the covariates. |
| title | Rethinking Distance Metrics for Counterfactual Explainability |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.14522 |