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• W3033662131 abstract "We propose to address the task of causal structure learning from data in a supervised manner. Existing work on learning causal directions by supervised learning is restricted to learning pairwise relation, and not well suited for whole DAG discovery. We propose a novel approach of modeling the whole DAG structure discovery as a supervised learning. To fit the problem in hand, we propose to use permutation equivariant models that align well with the problem domain. We evaluate the proposed approach extensively on synthetic graphs of size 10,20,50,100 and real data, and show promising results compared with a variety of previous approaches." @default.
• W3033662131 created "2020-06-12" @default.
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• W3033662131 date "2020-06-08" @default.
• W3033662131 modified "2023-10-11" @default.
• W3033662131 title "Supervised Whole DAG Causal Discovery." @default.
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• W3033662131 hasPublicationYear "2020" @default.
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