Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.02878 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910949167857664 |
|---|---|
| author | Chen, Yizuo Bhatia, Amit |
| author_facet | Chen, Yizuo Bhatia, Amit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02878 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling and Discovering Direct Causes for Predictive Models Chen, Yizuo Bhatia, Amit Machine Learning Artificial Intelligence Methodology 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. |
| title | Modeling and Discovering Direct Causes for Predictive Models |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2412.02878 |