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Supervised Whole DAG Causal Discovery
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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.
Forward citations
Cited by 2 Pith papers
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A Meta-Learning Approach to Bayesian Causal Discovery
A meta-learning model, BCNP, learns to sample DAGs from an approximate Bayesian posterior over causal graphs, enforcing acyclicity and permutation equivariance by construction.
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Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms
Causal discovery evaluations should report negative control baselines, because common metrics can look good under random guessing.
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