A Sample-Efficient Conditional Independence Test in the Presence of Discretization
Sun, Boyang Yao, Yu Dong, Xinshuai Liu, Zongfang Liu, Tongliang Qiu, Yumou Zhang, Kun
Sun, Boyang
Yao, Yu
Dong, Xinshuai
Liu, Zongfang
Liu, Tongliang
Qiu, Yumou
Zhang, Kun
Supervisor
Department
Machine Learning
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
In many real-world scenarios, interested variables are often represented as discretized values due to measurement limitations. Applying Conditional Independence (CI) tests directly to such discretized data, however, can lead to incorrect conclusions. To address this, recent advancements have sought to infer the correct CI relationship between the latent variables through binarizing observed data. However, this process inevitably results in a loss of information, which degrades the test’s performance. Motivated by this, this paper introduces a sample-efficient CI test that does not rely on the binarization process. We find that the independence relationships of latent continuous variables can be established by addressing an over-identifying restriction problem with Generalized Method of Moments (GMM). Based on this insight, we derive an appropriate test statistic and establish its asymptotic distribution correctly reflecting CI by leveraging nodewise regression. Theoretical findings and Empirical results across various datasets demonstrate that the superiority and effectiveness of our proposed test. Our code implementation is provided in https://github.com/boyangaaaaa/DCT.
Citation
B. Sun et al., “A Sample Efficient Conditional Independence Test in the Presence of Discretization,” Oct. 06, 2025, PMLR. [Online]. Available: https://proceedings.mlr.press/v267/sun25y.html
Source
Proceedings of Machine Learning Research
Conference
42nd International Conference on Machine Learning, ICML 2025
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Source
42nd International Conference on Machine Learning, ICML 2025
Publisher
ML Research Press