RcausalEGM: A General Causal Inference Framework by Encoding Generative Modeling

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


Reference manual: RcausalEGM.pdf
Vignettes: Binary Treatment
Continous Treatment


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


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