Abstract:Identifying the structure of a partially observed causal system is essential to various scientific fields. Recent advances have focused on constraint-based causal discovery to solve this problem, and yet in practice these methods often face challenges related to multiple testing and error propagation. These issues could be mitigated by a score-based method and thus it has raised great attention whether there exists a score-based greedy search method that can handle the partially observed scenario. In this work, we propose the first score-based greedy search method for the identification of structure involving latent variables with identifiability guarantees. Specifically, we propose Generalized N Factor Model and establish the global consistency:
the true structure including latent variables can be identified up to the Markov equivalence class by using score. We then design
Latent variable Greedy Equivalence Search (LGES), a greedy search algorithm for this class of model with well-defined operators,
which search very efficiently over the graph space to find the optimal structure. Our experiments on both synthetic and real-life data validate the effectiveness of our method (code will be publicly available).
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2510.04378 [cs.LG] |
| (or arXiv:2510.04378v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2510.04378 arXiv-issued DOI via DataCite |
From: Xinshuai Dong [view email]
[v1]
Sun, 5 Oct 2025 21:50:17 UTC (2,696 KB)
[v2]
Fri, 1 May 2026 03:23:51 UTC (2,694 KB)
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