arXiv (OAI Expanded)
open access
Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency. The oracle design is a covariate-dependent Neyman rule governed by unknown arm-conditional outcome variances. We investigate whether this sequential variance
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation#925193
- Identification and Estimation of Optimal Continuous Treatment Effects#1013242
- Variational objectives for amortized Bayesian inference in inverse problems: The role of posterior conditioning#1051638
- proxymate: Diagnosis and Adjustment of Proxy Estimates for Reliable Inference#405673
- Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference#120537
Record · ID 920958
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive