arXiv (All)
open access
Ranking Treatment Saturations: Regret Bounds and Experimental Design
We study the statistical decision problem of ranking treatment saturations, the treated shares of a cluster. Planners need a ranking, not only the best saturation, when rollouts are phased or the preferred saturation becomes infeasible. Using a two-stage randomized saturation experiment, we analyse
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
- SAT Prep Black Book - 2015 Edition: The Most Effective SAT Strategies Ever Published#1290681
- Saturation in G: simple & robust causal inference in cluster randomized trials with informative cluster sizes#793173
- Saturation in G: simple & robust causal inference in cluster randomized trials with informative cluster sizes#789169
- Generative diffusion models for spatiotemporal influenza forecasting#1070813
- Bidirectional Preference Synthesis: Learning Prompt-Conditioned Preferences from Boundary Failures#1199979
Record · ID 1389323
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