Papers › Supervised Whole DAG Causal Discovery

Supervised Whole DAG Causal Discovery

8 Jun 2020arXiv:2006.04697archive 2025-07-28

Hebi Li, Qi Xiao, Jin Tian

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.

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