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Creators/Authors contains: "Lou, Xingmei"

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  1. ; ; (, Transactions on Machine Learning Research (TMLR))
    We study learning the directed acyclic graph (DAG) for linear structural equation models (SEMs) when the causal structure is a polytree. Under Gaussian polytree models, we derive sufficient sample-size conditions under which the Chow–Liu algorithm exactly recovers both the skeleton and the equivalence class (CPDAG). Matching information-theoretic lower bounds provide necessary conditions, yielding sharp characterizations of problem difficulty. We further analyze inverse correlation matrix estimation with error bounds depending on dimension and the number of v-structures, and extend to group linear polytrees. Comprehensive simulations and benchmark experiments demonstrate robustness when true graphs are only approximately polytrees. 
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    Free, publicly-accessible full text available March 1, 2026