arXiv (All)
open access
Saturation in G: simple & robust causal inference in cluster randomized trials with informative cluster sizes
Cluster randomized trials (CRTs) can exhibit informative cluster sizes (ICS) where cluster size is associated with outcomes and/or treatment effects. Under ICS, the individual and cluster-average treatment effects (iATE, cATE) can diverge, and the conventional linear mixed-effects model (LMM) and ge
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
- Saturation in G: simple & robust causal inference in cluster randomized trials with informative cluster sizes#789169
- Causal Foundation Models#925376
- Causal Foundation Models#921140
- Network threats to causal inference: Variations in network position by participation in randomized controlled trials.#143309
- Sex-based prognosis in industry-sponsored advanced solid tumor trials: an individual participant data meta-analysis of survival and adverse events.#351701
Record · ID 793173
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