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We consider the problem of estimating a large causal polytree from arelatively small i.i.d. sample. This is motivated by the problem of determiningcausal structure when the number of variables is very large compared to thesample size, such as in gene regulatory networks. We give an algorithm thatrecovers the tree with high accuracy in such settings. The algorithm worksunder essentially no distributional or modeling assumptions other than somemild non-degeneracy conditions.

Estimating large causal polytrees from small samples