Supervised Whole DAG Causal Discovery
This addresses the challenge of causal discovery for researchers and practitioners by extending supervised learning beyond pairwise relations to whole DAGs, though it appears incremental as it builds on existing supervised approaches.
The paper tackles the problem of learning entire directed acyclic graph (DAG) causal structures from data by framing it as a supervised learning task, using permutation equivariant models, and demonstrates promising results on synthetic graphs up to size 100 and real data compared to previous methods.
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.