Conditional Independent Test in the Presence of Measurement Error with Causal Structure Learning

Zhang, Hongbin; Chen, Kezhou; Lin, Nankai; Yang, Aimin; Hao, Zhifeng; Chen, Zhengming

doi:10.24963/ijcai.2025/1013

Conditional Independent Test in the Presence of Measurement Error with Causal Structure Learning

Hongbin Zhang, Kezhou Chen, Nankai Lin, Aimin Yang, Zhifeng Hao, Zhengming Chen

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 9112-9120.

https://doi.org/10.24963/ijcai.2025/1013

Keywords

Uncertainty in AI: UAI: Bayesian networks
Uncertainty in AI: UAI: Causality, structural causal models and causal inference
Machine Learning: ML: Causality
Uncertainty in AI: UAI: Graphical models

Abstract

Testing conditional independence is a critical task, particularly in causal discovery and learning in Bayesian networks. However, in many real-world scenarios, variables are often measured with errors, such as those introduced by insufficient measurement accuracy, complicating the testing process. This paper focuses on testing conditional independence in the linear non-Gaussian measurement error model, under the condition that measurement error noise follows a Gaussian distribution. By leveraging high-order cumulants, we derive rank constraints on the cumulant matrix and establish their role in effectively assessing conditional independence, even in the presence of measurement errors. Based on these theoretical results, we leverage the rank constraints of the cumulant matrix as a tool for conditional independence testing and incorporate it into the PC algorithm, resulting in the PC-ME algorithm — a method designed to learn causal structures from observed data while accounting for measurement errors. Experimental results demonstrate that the proposed method outperforms existing approaches, particularly in cases other methods encounter difficulties.

Hongbin Zhang, Kezhou Chen, Nankai Lin, Aimin Yang, Zhifeng Hao, and Zhengming Chen. 2025. Conditional Independent Test in the Presence of Measurement Error with Causal Structure Learning. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 9112–9120. https://doi.org/10.24963/ijcai.2025/1013

@inproceedings{ijcai2025p1013,
  title     = {Conditional Independent Test in the Presence of Measurement Error with Causal Structure Learning},
  author    = {Zhang, Hongbin and Chen, Kezhou and Lin, Nankai and Yang, Aimin and Hao, Zhifeng and Chen, Zhengming},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {9112--9120},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/1013},
  url       = {https://doi.org/10.24963/ijcai.2025/1013},
}

TY  - CPAPER
TI  - Conditional Independent Test in the Presence of Measurement Error with Causal Structure Learning
AU  - Zhang, Hongbin
AU  - Chen, Kezhou
AU  - Lin, Nankai
AU  - Yang, Aimin
AU  - Hao, Zhifeng
AU  - Chen, Zhengming
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 9112
EP  - 9120
DO  - 10.24963/ijcai.2025/1013
UR  - https://doi.org/10.24963/ijcai.2025/1013
ER  -