The 'AIPW' package 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). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology. <doi:10.1093/aje/kwab207>". Visit: <https://yqzhong7.github.io/AIPW/> for more information.
Version: 0.6.9.2 Depends: R (≥ 2.10) Imports: stats, utils, R6, SuperLearner, ggplot2, future.apply, progressr, Rsolnp Suggests: testthat (≥ 2.1.0), knitr, rmarkdown, covr, tmle Published: 2025-04-05 DOI: 10.32614/CRAN.package.AIPW Author: Yongqi Zhong [aut, cre], Ashley Naimi [aut], Gabriel Conzuelo [ctb], Edward Kennedy [ctb] Maintainer: Yongqi Zhong <yq.zhong7 at gmail.com> BugReports: https://github.com/yqzhong7/AIPW/issues License: GPL-3 URL: https://github.com/yqzhong7/AIPW NeedsCompilation: no Language: es Citation: AIPW citation info Materials: README NEWS In views: CausalInference CRAN checks: AIPW results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=AIPW to link to this page.
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