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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2505.02435 |
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| _version_ | 1866918030673444864 |
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| author | Fatemi, Pouria Sharifian, Ehsan Yassaee, Mohammad Hossein |
| author_facet | Fatemi, Pouria Sharifian, Ehsan Yassaee, Mohammad Hossein |
| contents | Counterfactual explanations enhance interpretability by identifying alternative inputs that produce different outputs, offering localized insights into model decisions. However, traditional methods often neglect causal relationships, leading to unrealistic examples. While newer approaches integrate causality, they are computationally expensive. To address these challenges, we propose an efficient method called BRACE based on backtracking counterfactuals that incorporates causal reasoning to generate actionable explanations. We first examine the limitations of existing methods and then introduce our novel approach and its features. We also explore the relationship between our method and previous techniques, demonstrating that it generalizes them in specific scenarios. Finally, experiments show that our method provides deeper insights into model outputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02435 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability Fatemi, Pouria Sharifian, Ehsan Yassaee, Mohammad Hossein Machine Learning Artificial Intelligence Counterfactual explanations enhance interpretability by identifying alternative inputs that produce different outputs, offering localized insights into model decisions. However, traditional methods often neglect causal relationships, leading to unrealistic examples. While newer approaches integrate causality, they are computationally expensive. To address these challenges, we propose an efficient method called BRACE based on backtracking counterfactuals that incorporates causal reasoning to generate actionable explanations. We first examine the limitations of existing methods and then introduce our novel approach and its features. We also explore the relationship between our method and previous techniques, demonstrating that it generalizes them in specific scenarios. Finally, experiments show that our method provides deeper insights into model outputs. |
| title | A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.02435 |