Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models

• URL: http://arxiv.org/abs/2510.04378v1
• Date: Sun, 05 Oct 2025 21:50:17 GMT
• Title: Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
• Authors: Xinshuai Dong, Ignavier Ng, Haoyue Dai, Jiaqi Sun, Xiangchen Song, Peter Spirtes, Kun Zhang,
• Abstract summary: We propose the first score-based greedy search method for the identification of structure involving latent variables with identifiability guarantees.<n>We then design Latent variable Greedy Equivalence Search (LGES), a greedy search algorithm for this class of model with well-defined operators.
• Score: 34.09555821357439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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).

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