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When and Why LLM Causal Priors Help: Closed-Loop Prior Selection for Amortized Causal Inference
Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundational task. Prior-data fitted networks (PFNs) amortize the task: a model trained on large numbers of programmatically generated synthetic causal tasks
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