From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Maps
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916862861770752 |
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| author | He, Chenxuan Gao, Yuan Zhu, Liping Huang, Jian |
| author_facet | He, Chenxuan Gao, Yuan Zhu, Liping Huang, Jian |
| contents | Testing conditional independence between two random vectors given a third is a fundamental and challenging problem in statistics, particularly in multivariate nonparametric settings due to the complexity of conditional structures. We propose a novel method for testing conditional independence by transforming it to an unconditional independence test problem. We achieve this by constructing two transport maps that transform conditional independence into unconditional independence, this substantially simplifies the problem. These transport maps are estimated from data using conditional continuous normalizing flow models. Within this framework, we derive a test statistic and prove its asymptotic validity under both the null and alternative hypotheses. A permutation-based procedure is employed to evaluate the significance of the test. We validate the proposed method through extensive simulations and real-data analysis. Our numerical studies demonstrate the practical effectiveness of the proposed method for conditional independence |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Maps He, Chenxuan Gao, Yuan Zhu, Liping Huang, Jian Machine Learning Methodology 62G05, 62G08, 68T07 Testing conditional independence between two random vectors given a third is a fundamental and challenging problem in statistics, particularly in multivariate nonparametric settings due to the complexity of conditional structures. We propose a novel method for testing conditional independence by transforming it to an unconditional independence test problem. We achieve this by constructing two transport maps that transform conditional independence into unconditional independence, this substantially simplifies the problem. These transport maps are estimated from data using conditional continuous normalizing flow models. Within this framework, we derive a test statistic and prove its asymptotic validity under both the null and alternative hypotheses. A permutation-based procedure is employed to evaluate the significance of the test. We validate the proposed method through extensive simulations and real-data analysis. Our numerical studies demonstrate the practical effectiveness of the proposed method for conditional independence |
| title | From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Maps |
| topic | Machine Learning Methodology 62G05, 62G08, 68T07 |
| url | https://arxiv.org/abs/2504.09567 |