Learning DAGs Graph Model from Sparse Inverse Covariance

• Item type: SIG Technical Reports(1)
• 公開日: 2022-06-20
• タイトル: Learning DAGs Graph Model from Sparse Inverse Covariance
• タイトル / 言語: en
• タイトル: Learning DAGs Graph Model from Sparse Inverse Covariance
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Department of Information Science and Technology, The University of Tokyo
• 著者所属: Department of Information Science and Technology, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Department of Information Science and Technology, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Department of Information Science and Technology, The University of Tokyo
• 著者名: Ziyu, Guo; Shin, Matsushima
• 著者名(英): Ziyu, Guo; Shin, Matsushima
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Learning DAG-like causal graphs derived from structural equation models (SEMs) using data without intervention is one of the most important tasks in causal inference. However, in the real world, given observed data, the SEMs might be non-identifiable which makes the problem become hard. Recently, some studies have focused on inference on both identifiable and non-identifiable SEMs. These studies perform exhaustive search on all possible causal orders or set constraints on DAG. In this paper, we focus on learning SEMs without identifiability and propose an approach that estimates DAG computed by Cholesky decomposition of permutated precision matrix with BIC criterion. We perform graphical lasso to estimate sparse inverse covariance, which also controls the sparsity of the graph within l1 norm. Besides, we perform exhaustive search on all possible causal structures and use BIC criterion to construct the estimated DAG. Our experiments validate the performance of our approach on the SEMs without identifiability.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Learning DAG-like causal graphs derived from structural equation models (SEMs) using data without intervention is one of the most important tasks in causal inference. However, in the real world, given observed data, the SEMs might be non-identifiable which makes the problem become hard. Recently, some studies have focused on inference on both identifiable and non-identifiable SEMs. These studies perform exhaustive search on all possible causal orders or set constraints on DAG. In this paper, we focus on learning SEMs without identifiability and propose an approach that estimates DAG computed by Cholesky decomposition of permutated precision matrix with BIC criterion. We perform graphical lasso to estimate sparse inverse covariance, which also controls the sparsity of the graph within l1 norm. Besides, we perform exhaustive search on all possible causal structures and use BIC criterion to construct the estimated DAG. Our experiments validate the performance of our approach on the SEMs without identifiability.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12055912
• 書誌情報: 研究報告バイオ情報学（BIO）; 巻 2022-BIO-70, 号 25, p. 1-6, 発行日 2022-06-20
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8590
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会