Learning Linear Non-Gaussian Polytree ModelsDownload PDF

Published: 20 May 2022, Last Modified: 22 Oct 2023UAI 2022 PosterReaders: Everyone
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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