  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.
