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Research questionHow can sequential treatment allocation learn unknown outcome variances while preserving efficient ATE inference?In adaptive experiments, treatment probabilities must respond to observed outcomes because arm-specific variances are initially unknown. Poor allocation can waste samples, while adaptation must still support precise and valid average treatment effect estimates.
AI
Economics
Machine Learning
Research Paper
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE EstimationThe source studies transformer policies that imitate a Bayesian posterior Neyman allocation rule using experimental histories and potential-outcome beliefs. It also addresses unknown outcome smoothness through a mixture-of-experts policy, with theoretical learnability results and experiments on teacher imitation, adaptive allocation, and ATE precision.research paper · Sep 2, 2026
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