arXiv (All)
open access
confoundvis for Visualizing and Reporting Sensitivity to Unmeasured Confounding in R
Sensitivity analysis asks how strong unmeasured confounding would have to be before an observational conclusion changes. Established methods answer this question using different quantities, including partial $R^2$ values, correlation products, and risk ratios. In this article, we present confoundvis
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
- Bounding the Risk Difference Bias Given Incomplete Parameters for Externally Adjusting Uncontrolled Confounding#1051391
- Measuring the Sensitivity of Classification Models with the Error Sensitivity Profile#141481
- Calibrating Ambiguity Set via Diagnostic Transport for Distributionally Robust Optimization#1247720
- Category-Specific Sensitivity Allows Modeling Variability Effects in a Similarity Framework#171677
- Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations#1143226
Record · ID 1298227
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