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RcausalEGM: A General Causal Inference Framework by Encoding GenerativeModeling

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 + fileLICENSE
URL:https://github.com/SUwonglab/CausalEGM
NeedsCompilation:no
Materials:NEWS
CRAN checks:RcausalEGM results

Documentation:

Reference manual:RcausalEGM.html ,RcausalEGM.pdf
Vignettes:Binary Treatment (source,R code)
Continous Treatment (source,R code)

Downloads:

Package source: RcausalEGM_0.3.3.tar.gz
Windows binaries: r-devel:RcausalEGM_0.3.3.zip, r-release:RcausalEGM_0.3.3.zip, r-oldrel:RcausalEGM_0.3.3.zip
macOS binaries: r-release (arm64):RcausalEGM_0.3.3.tgz, r-oldrel (arm64):RcausalEGM_0.3.3.tgz, r-release (x86_64):RcausalEGM_0.3.3.tgz, r-oldrel (x86_64):RcausalEGM_0.3.3.tgz

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=RcausalEGMto link to this page.


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