senseweight implements a set of sensitivity functions and tools to help researchers transparently conduct sensitivity analyses for weighted estimators. senseweight allows researchers to assess the sensitivity present in their weighted estimates to omitted confounders. Specific methods provided in senseweight include the following: (1) visualization tools to summarize sensitivity; (2) summary tables containing necessary sensitivity statistics; (3) formal benchmarking methods which allow researchers to use observed covariates to assess the plausibility of different confounders.
Installation
You can install the development version of senseweight from GitHub with:
# install.packages("devtools")
devtools::install_github("melodyyhuang/senseweight")References
The package implements a series of methods developed in the following papers.
For the technical introduction of the sensitivity tools:
- Huang, Melody. “Sensitivity Analysis in the Generalization of Experimental Results.” Journal of the Royal Statistical Society Series A: Statistics in Society (2024)
- Hartman, Erin and Huang, Melody. “Sensitivity Analysis for Survey Weights.” Political Analysis (2024)
For less technical introductions with interesting applications and best practice:
- Huang, Melody and Hartman, Erin. “Assessing Nonignorable Response: Sensitivity Analysis for Survey Weighting, with Applications to Survey Estimates of COVID-19 Vaccination Uptake.” Working paper.
- Bailey, Michael. “Polling at a Crossroads.” (Chapter 7)