# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "causens" in publications use:' type: software license: MIT title: 'causens: Perform Causal Sensitivity Analyses Using Various Statistical Methods' version: 0.0.3 doi: 10.32614/CRAN.package.causens abstract: '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) and Li, Shen, Wu, and Li (2011) ), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) ).' authors: - family-names: Dong given-names: Larry email: larry.dong@mail.utoronto.ca orcid: https://orcid.org/0000-0001-7775-7798 - family-names: Zou given-names: Yushu email: yushu.zou@mail.utoronto.ca orcid: https://orcid.org/0009-0004-1133-4724 - family-names: Liu given-names: Kuan email: kuan.liu@utoronto.ca orcid: https://orcid.org/0000-0002-5017-1276 repository: https://kuan-liu-lab.r-universe.dev repository-code: https://github.com/Kuan-Liu-Lab/causens commit: a5c37d7fc5d8ac1e5dc8be9c14a41f49a55ae5f2 url: https://kuan-liu-lab.github.io/causens/ date-released: '2025-06-03' contact: - family-names: Dong given-names: Larry email: larry.dong@mail.utoronto.ca orcid: https://orcid.org/0000-0001-7775-7798