Under a faithfulness assumption, causal order among observed variables in linear non-Gaussian systems with latent confounders is identifiable, and all observationally equivalent causal effect matrices can be enumerated in polynomial time.
Causality in linear nongaussian acyclic models in the presence of latent gaussian confounders
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Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
Under a faithfulness assumption, causal order among observed variables in linear non-Gaussian systems with latent confounders is identifiable, and all observationally equivalent causal effect matrices can be enumerated in polynomial time.