Proposes a sequential causal discovery framework integrating noisy LM priors with batch data via PAG representation and adaptive edge querying for improved structural accuracy.
Review of causal discovery methods based on graphical models
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Sequential Causal Discovery with Noisy Language Model Priors
Proposes a sequential causal discovery framework integrating noisy LM priors with batch data via PAG representation and adaptive edge querying for improved structural accuracy.