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.
By Property 2, collider-connectedness between X and Y is equivalent to Y belonging to the Markov blanket ofXinM
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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.