A moment-based algorithm consistently recovers cycle-disjoint linear non-Gaussian causal graphs with feedback loops, together with a full characterization of distribution-equivalent graphs.
Characterizing distribution equivalence and structure learning for cyclic and acyclic directed graphs
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Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles
A moment-based algorithm consistently recovers cycle-disjoint linear non-Gaussian causal graphs with feedback loops, together with a full characterization of distribution-equivalent graphs.