Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866918000273129472 |
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| author | Machado, Agathe Fernandes Charpentier, Arthur Gallic, Ewen |
| author_facet | Machado, Agathe Fernandes Charpentier, Arthur Gallic, Ewen |
| contents | In this paper, we link two existing approaches to derive counterfactuals: adaptations based on a causal graph, and optimal transport. We extend "Knothe's rearrangement" and "triangular transport" to probabilistic graphical models, and use this counterfactual approach, referred to as sequential transport, to discuss fairness at the individual level. After establishing the theoretical foundations of the proposed method, we demonstrate its application through numerical experiments on both synthetic and real datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03425 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness Machado, Agathe Fernandes Charpentier, Arthur Gallic, Ewen Machine Learning Methodology In this paper, we link two existing approaches to derive counterfactuals: adaptations based on a causal graph, and optimal transport. We extend "Knothe's rearrangement" and "triangular transport" to probabilistic graphical models, and use this counterfactual approach, referred to as sequential transport, to discuss fairness at the individual level. After establishing the theoretical foundations of the proposed method, we demonstrate its application through numerical experiments on both synthetic and real datasets. |
| title | Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2408.03425 |