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.
Nonlinear causal discovery with additive noise models
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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.