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causens  

Perform Causal Sensitivity Analyses Using Various Statistical Methods
View on CRAN: Click here


Download and install causens package within the R console
Install from CRAN:
install.packages("causens")

Install from Github:
library("remotes")
install_github("cran/causens")

Install by package version:
library("remotes")
install_version("causens", "0.0.3")



Attach the package and use:
library("causens")
Maintained by
Larry Dong
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-06-05
Latest Update: 2025-06-05
Description:
While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In 'causens', three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) <doi:10.1002/sim.1657> and Li, Shen, Wu, and Li (2011) <doi:10.1093/aje/kwr096>), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) <doi:10.1002/sim.7298>).
How to cite:
Larry Dong (2025). causens: Perform Causal Sensitivity Analyses Using Various Statistical Methods. R package version 0.0.3, https://cran.r-project.org/web/packages/causens. Accessed 12 Jun. 2025.
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