Estimating large causal polytrees from small samples

arXiv:2209.07028v3 Announce Type: replace-cross
Abstract: We consider the problem of estimating 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.

abstract arxiv causal cs.lg gene math.pr math.st networks regulatory sample samples small stat.me stat.ml stat.th type variables