CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. A description of the methods is given in Liu (2022) <doi:10.48550/arXiv.2212.05925>.
Version: 0.3.3 Depends: R (≥ 3.6.0) Imports: reticulate Suggests: rmarkdown, knitr, testthat (≥ 3.0.0) Published: 2023-03-28 DOI: 10.32614/CRAN.package.RcausalEGM Author: Qiao Liu [aut, cre], Wing Wong [aut], Balasubramanian Narasimhan [ctb] Maintainer: Qiao Liu <liuqiao at stanford.edu> BugReports: https://github.com/SUwonglab/CausalEGM/issues License: MIT + file LICENSE URL: https://github.com/SUwonglab/CausalEGM NeedsCompilation: no Materials: NEWS CRAN checks: RcausalEGM results Documentation: Reference manual: RcausalEGM.pdf Vignettes: Binary Treatment (source, R code)Please use the canonical form https://CRAN.R-project.org/package=RcausalEGM to link to this page.
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