Estimating large causal polytrees from small samples
Methodology
2024-08-20 v4 Machine Learning
Probability
Statistics Theory
Machine Learning
Statistics Theory
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.
Cite
@article{arxiv.2209.07028,
title = {Estimating large causal polytrees from small samples},
author = {Sourav Chatterjee and Mathukumalli Vidyasagar},
journal= {arXiv preprint arXiv:2209.07028},
year = {2024}
}
Comments
27 pages. To appear in the Indian J. Pure Appl. Math, special issue in honor of Prof. K. R. Parthasarathy