Learning Linear Non-Gaussian Polytree Models

Daniele Tramontano; Anthea Monod; Mathias Drton

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arXiv · 2208.06701

Learning Linear Non-Gaussian Polytree Models

Daniele Tramontano·Anthea Monod·Mathias Drton

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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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Daniele Tramontano, Anthea Monod, Mathias Drton. 2022-08-13. Learning Linear Non-Gaussian Polytree Models. https://arxiv.org/abs/2208.06701

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