arXiv (All)
open access
Design-Driven Inference for Online Experiments: Jointly Optimal Evidence Collection, Testing, and Estimation
Online multifactorial experiments requires early stopping for practically negligible effects while controlling false stopping under continuous monitoring and retaining precise treatment-effect estimation. We develop a design-driven framework that chooses allocation and the evidence rule jointly, tre
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
- Evidence-Inference Reconstruction: When The Evidence Is Recalled But The Reasoning Goes Wrong#1118851
- Comment on arXiv:2607.01233: Survivorship Bias in Published-Paper Baselines for Research-Idea Distributions#1110394
- YouRA: A Persistent-State Architecture for Evidence-Traceable Autonomous Research Agents#1156032
- ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence#1325277
- Toward Reliable and Scalable Real-World Evidence: Principled AI-enabled Methods for Evidence Generation and Clinical Trial Design#281902
Record · ID 1217206
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