In linear non-Gaussian models, high-dimensional cyclic causal structures can be coarsened to a low-dimensional DAG that is invariant across observational equivalence classes and learnable in cubic time.
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Coarsening Linear Non-Gaussian Causal Models with Cycles
In linear non-Gaussian models, high-dimensional cyclic causal structures can be coarsened to a low-dimensional DAG that is invariant across observational equivalence classes and learnable in cubic time.