Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918476654837760 |
|---|---|
| author | Dong, Xinshuai Ng, Ignavier Dai, Haoyue Sun, Jiaqi Song, Xiangchen Spirtes, Peter Zhang, Kun |
| author_facet | Dong, Xinshuai Ng, Ignavier Dai, Haoyue Sun, Jiaqi Song, Xiangchen Spirtes, Peter Zhang, Kun |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models Dong, Xinshuai Ng, Ignavier Dai, Haoyue Sun, Jiaqi Song, Xiangchen Spirtes, Peter Zhang, Kun Machine Learning 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). |
| title | Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.04378 |