Related papers: Learning Linear Non-Gaussian Polytree Models

Learning Linear Non-Gaussian Polytree Models

URL: http://arxiv.org/abs/2208.06701v1
Date: Sat, 13 Aug 2022 18:20:10 GMT
Title: Learning Linear Non-Gaussian Polytree Models
Authors: Daniele Tramontano, Anthea Monod, Mathias Drton
Abstract summary: We propose new algorithms to efficiently learn graphs that are polytrees. Our approach combines the Chow--Liu algorithm, which first learns the undirected tree structure, with novel schemes to orient the edges.
Score: 2.4493299476776778
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In the context of graphical causal discovery, we adapt the versatile framework of linear non-Gaussian acyclic models (LiNGAMs) to propose new algorithms to efficiently learn graphs that are polytrees. Our approach combines the Chow--Liu algorithm, which first learns the undirected tree structure, with novel schemes to orient the edges. The orientation schemes assess algebraic relations among moments of the data-generating distribution and are computationally inexpensive. We establish high-dimensional consistency results for our approach and compare different algorithmic versions in numerical experiments.

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