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.
You can install the development version of senseweight from GitHub with:
# install.packages("devtools")
devtools::install_github("melodyyhuang/senseweight")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)