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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

Documentation:

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

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 form https://CRAN.R-project.org/package=RcausalEGM to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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