Score-based Generative Modeling for Conditional Independence Testing
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915312501260288 |
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| author | Ren, Yixin Jin, Chenghou Xia, Yewei Ke, Li Huang, Longtao Xue, Hui Zhang, Hao Guan, Jihong Zhou, Shuigeng |
| author_facet | Ren, Yixin Jin, Chenghou Xia, Yewei Ke, Li Huang, Longtao Xue, Hui Zhang, Hao Guan, Jihong Zhou, Shuigeng |
| contents | Determining conditional independence (CI) relationships between random variables is a fundamental yet challenging task in machine learning and statistics, especially in high-dimensional settings. Existing generative model-based CI testing methods, such as those utilizing generative adversarial networks (GANs), often struggle with undesirable modeling of conditional distributions and training instability, resulting in subpar performance. To address these issues, we propose a novel CI testing method via score-based generative modeling, which achieves precise Type I error control and strong testing power. Concretely, we first employ a sliced conditional score matching scheme to accurately estimate conditional score and use Langevin dynamics conditional sampling to generate null hypothesis samples, ensuring precise Type I error control. Then, we incorporate a goodness-of-fit stage into the method to verify generated samples and enhance interpretability in practice. We theoretically establish the error bound of conditional distributions modeled by score-based generative models and prove the validity of our CI tests. Extensive experiments on both synthetic and real-world datasets show that our method significantly outperforms existing state-of-the-art methods, providing a promising way to revitalize generative model-based CI testing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Score-based Generative Modeling for Conditional Independence Testing Ren, Yixin Jin, Chenghou Xia, Yewei Ke, Li Huang, Longtao Xue, Hui Zhang, Hao Guan, Jihong Zhou, Shuigeng Machine Learning Artificial Intelligence Determining conditional independence (CI) relationships between random variables is a fundamental yet challenging task in machine learning and statistics, especially in high-dimensional settings. Existing generative model-based CI testing methods, such as those utilizing generative adversarial networks (GANs), often struggle with undesirable modeling of conditional distributions and training instability, resulting in subpar performance. To address these issues, we propose a novel CI testing method via score-based generative modeling, which achieves precise Type I error control and strong testing power. Concretely, we first employ a sliced conditional score matching scheme to accurately estimate conditional score and use Langevin dynamics conditional sampling to generate null hypothesis samples, ensuring precise Type I error control. Then, we incorporate a goodness-of-fit stage into the method to verify generated samples and enhance interpretability in practice. We theoretically establish the error bound of conditional distributions modeled by score-based generative models and prove the validity of our CI tests. Extensive experiments on both synthetic and real-world datasets show that our method significantly outperforms existing state-of-the-art methods, providing a promising way to revitalize generative model-based CI testing. |
| title | Score-based Generative Modeling for Conditional Independence Testing |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.23309 |