{"ID":2860425,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.04378","arxiv_id":"2510.04378","title":"Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models","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).","short_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 coul...","url_abs":"https://arxiv.org/abs/2510.04378","url_pdf":"https://arxiv.org/pdf/2510.04378v2","authors":"[\"Xinshuai Dong\",\"Ignavier Ng\",\"Haoyue Dai\",\"Jiaqi Sun\",\"Xiangchen Song\",\"Peter Spirtes\",\"Kun Zhang\"]","published":"2025-10-05T21:50:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}