Estimating large causal polytree skeletons from small samples

Sourav Chatterjee, Mathukumalli Vidyasagar

We consider the problem of estimating the skeleton of a large causal polytree from a relatively small i.i.d. sample. This is motivated by the problem of determining causal structure when the number of variables is very large compared to the sample size, such as in gene regulatory networks. We give an algorithm that recovers the tree with high accuracy in such settings. The algorithm works under essentially no distributional or modeling assumptions other than some mild non-degeneracy conditions.

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