When and Why LLM Causal Priors Help: Closed-Loop Prior Selection for Amortized Causal Inference
A closed-loop prior selection framework is proposed that casts prior injection as a budget-constrained optimization over a candidate prior pool and shows that the use of LLM causal priors stops being manual trial and error and becomes an empirically verifiable selection problem.
Hao-Hao Zhou
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