DiCoLa recursively decomposes causal discovery tasks into smaller subproblems solvable with existing algorithms and reconstructs the global structure, with proven soundness and completeness even in the presence of latent variables.
These results confirm that DICOLAis most beneficial on sparse or decomposable graphs and gradually approaches the behavior of the base learner on denser graphs
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A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
DiCoLa recursively decomposes causal discovery tasks into smaller subproblems solvable with existing algorithms and reconstructs the global structure, with proven soundness and completeness even in the presence of latent variables.