Modeling and Discovering Direct Causes for Predictive Models
We introduce a causal modeling framework that captures the input-output behavior of predictive models (e.g., machine learning models). The framework enables us to identify features that directly cause the predictions, which has broad implications for data collection and model evaluation. We then pre...
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| Vydáno v: | Proceedings of the International Florida Artificial Intelligence Research Society Conference Ročník 38; číslo 1 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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LibraryPress@UF
14.05.2025
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| ISSN: | 2334-0754, 2334-0762 |
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| Abstract | We introduce a causal modeling framework that captures the input-output behavior of predictive models (e.g., machine learning models). The framework enables us to identify features that directly cause the predictions, which has broad implications for data collection and model evaluation. We then present sound and complete algorithms for discovering direct causes (from data) under some assumptions. Furthermore, we propose a novel independence rule that can be integrated with the algorithms to accelerate the discovery process as we demonstrate both theoretically and empirically. |
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| AbstractList | We introduce a causal modeling framework that captures the input-output behavior of predictive models (e.g., machine learning models). The framework enables us to identify features that directly cause the predictions, which has broad implications for data collection and model evaluation. We then present sound and complete algorithms for discovering direct causes (from data) under some assumptions. Furthermore, we propose a novel independence rule that can be integrated with the algorithms to accelerate the discovery process as we demonstrate both theoretically and empirically. |
| Author | Chen, Yizuo Bhatia, Amit |
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| Title | Modeling and Discovering Direct Causes for Predictive Models |
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