Latent Variable Causal Discovery under Selection Bias
Dai, Haoyue Qiu, Yiwen Ng, Ignavier Dong, Xinshuai Spirtes, Peter L. Zhang, Kun
Dai, Haoyue
Qiu, Yiwen
Ng, Ignavier
Dong, Xinshuai
Spirtes, Peter L.
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
Addressing selection bias in latent variable causal discovery is important yet underexplored, largely due to a lack of suitable statistical tools: While various tools beyond basic conditional indepen-dencies have been developed to handle latent vari-ables, none have been adapted for selection bias. We make an attempt by studying rank constraints, which, as a generalization to conditional inde-pendence constraints, exploits the ranks of co-variance submatrices in linear Gaussian models. We show that although selection can significantly complicate the joint distribution, interestingly, the ranks in the biased covariance matrices still pre-serve meaningful information about both causal structures and selection mechanisms. We provide a graph-theoretic characterization of such rank constraints. Using this tool, we demonstrate that the one-factor model, a classical latent variable model, can be identified under selection bias. Sim-ulations and real-world experiments confirm the effectiveness of using our rank constraints.
Citation
H. Dai, Y. Qiu, I. Ng, X. Dong, P. Spirtes, and K. Zhang, “Latent Variable Causal Discovery under Selection Bias,” Oct. 06, 2025, PMLR. [Online]. Available: https://proceedings.mlr.press/v267/dai25k.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