Sequential experiments achieve i.i.d.-level semiparametric efficiency via an induced average propensity score, attained by batched designs using influence-function regression adjustment or adaptive covariate balancing.
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Transformers trained to imitate Bayesian posterior Neyman allocations achieve smoothness-adaptive ATE estimation via mixture-of-experts in-context learning.
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Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
Transformers trained to imitate Bayesian posterior Neyman allocations achieve smoothness-adaptive ATE estimation via mixture-of-experts in-context learning.