confoundvis for Visualizing and Reporting Sensitivity to Unmeasured Confounding in R
Abstract
Sensitivity analysis asks how strong unmeasured confounding would have to be before an observational conclusion changes. Several R packages already calculate established sensitivity quantities, but their outputs use different parameters, objects, and plotting conventions. In this article, we describe confoundvis 0.3.0, an R package designed as a common interface for these analyses. The package stores results from the impact threshold of a confounding variable, the robustness of inference to replacement, partial-R^2 omitted-variable bias analysis, E-values, and Rosenbaum bounds for matched pairs in one confoundsens object. Each result keeps its own parameter and its own published formula. The same functions then draw robustness curves, locate the points at which a conclusion changes, write plain-language reports, and collect several analyses in one table. When a framework describes a confounder by two associations, the package also draws the two-parameter surface on which the familiar single number lies and, where such benchmarks are defined, places measured covariates on the surface. We demonstrate the package with the right heart catheterization data and with a matched-pair study of welders. The native calculations agree with sensemakr, konfound, DOS2, sensitivity2x2xk, and sensitivitymv to numerical or grid precision, and imported EValue results reproduce the reported E-values. The purpose of confoundvis is not to replace the underlying methods or to place their sensitivity parameters on a common scale. Its purpose is to provide a consistent workflow for drawing, comparing, and reporting the results that those methods produce.
Related
- Spatial causal inference in the presence of unmeasured confounding and interference
- Proximal Balancing for Causal Effect Estimation under Unmeasured Confounding
- Doubly valid and doubly sharp sensitivity analysis to unobserved confounding for survival outcomes
- Identification-Based Zero-Sum Tracking Control for Linear Composite Systems with Unmeasured Disturbances
- Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
BibTeX
@misc{hait2026confoundvis,
title = {{confoundvis for Visualizing and Reporting Sensitivity to Unmeasured Confounding in R}},
author = {Subir Hait},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.13731},
eprint = {2610.13731},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.13731},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - confoundvis for Visualizing and Reporting Sensitivity to Unmeasured Confounding in R
AU - Subir Hait
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2610.13731
UR - https://arxiv.org/abs/2610.13731
AB - Sensitivity analysis asks how strong unmeasured confounding would have to be before an observational conclusion changes. Several R packages already calculate established sensitivity quantities, but their outputs use different parameters, objects, and plotting conventions. In this article, we describe confoundvis 0.3.0, an R package designed as a common interface for these analyses. The package stores results from the impact threshold of a confounding variable, the robustness of inference to replacement, partial-R^2 omitted-variable bias analysis, E-values, and Rosenbaum bounds for matched pairs in one confoundsens object. Each result keeps its own parameter and its own published formula. The same functions then draw robustness curves, locate the points at which a conclusion changes, write plain-language reports, and collect several analyses in one table. When a framework describes a confounder by two associations, the package also draws the two-parameter surface on which the familiar single number lies and, where such benchmarks are defined, places measured covariates on the surface. We demonstrate the package with the right heart catheterization data and with a matched-pair study of welders. The native calculations agree with sensemakr, konfound, DOS2, sensitivity2x2xk, and sensitivitymv to numerical or grid precision, and imported EValue results reproduce the reported E-values. The purpose of confoundvis is not to replace the underlying methods or to place their sensitivity parameters on a common scale. Its purpose is to provide a consistent workflow for drawing, comparing, and reporting the results that those methods produce.
ER -