Learning Linear Non-Gaussian Polytree Models

Daniele Tramontano, Mathias Drton, Anthea Monod

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 ap-
proach and compare different algorithmic versions
in numerical experiments.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/learning-linear-non-gaussian-polytree-models/code)