AIPW: Augmented Inverse Probability Weighting

The 'AIPW' pacakge implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the 'AIPW' package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021, In Press). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology". Visit: <> for more information.

Depends: R (≥ 2.10)
Imports: stats, utils, R6, SuperLearner, ggplot2, future.apply, progressr, Rsolnp
Suggests: testthat (≥ 2.1.0), knitr, rmarkdown, covr, tmle
Published: 2021-06-11
DOI: 10.32614/CRAN.package.AIPW
Author: Yongqi Zhong ORCID iD [aut, cre], Ashley Naimi ORCID iD [aut], Gabriel Conzuelo [ctb], Edward Kennedy [ctb]
Maintainer: Yongqi Zhong <yq.zhong7 at>
License: GPL-3
NeedsCompilation: no
Language: es
Citation: AIPW citation info
Materials: README
In views: CausalInference
CRAN checks: AIPW results


Reference manual: AIPW.pdf
Vignettes: Getting Started with AIPW


Package source: AIPW_0.6.3.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): AIPW_0.6.3.2.tgz, r-oldrel (arm64): AIPW_0.6.3.2.tgz, r-release (x86_64): AIPW_0.6.3.2.tgz, r-oldrel (x86_64): AIPW_0.6.3.2.tgz

Reverse dependencies:

Reverse imports: RobinCar


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