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>).
Version: 0.0.3 Suggests: devtools, knitr, pkgdown, PSweight, rjags, rmarkdown, roxygen2, testthat (≥ 3.0.0), usethis, waldo Published: 2025-06-05 DOI: 10.32614/CRAN.package.causens Author: Larry Dong [aut, cre], Yushu Zou [aut], Kuan Liu [aut] Maintainer: Larry Dong <larry.dong at mail.utoronto.ca> BugReports: https://github.com/Kuan-Liu-Lab/causens/issues License: MIT + file LICENSE URL: https://kuan-liu-lab.github.io/causens/, https://github.com/Kuan-Liu-Lab/causens NeedsCompilation: no Materials: README CRAN checks: causens results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=causens to link to this page.
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