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Provides a pipeline for estimating the average treatment effect via semi-supervised learning. Outcome regression is fit with cross-fitting using various machine learning method or user customized function. Doubly robust ATE estimation leverages both labeled and unlabeled data under a semi-supervised missing-data framework. For more details see Hou et al. (2021) <doi:10.48550/arxiv.2110.12336>. A detailed vignette is included.
| Version: | 0.0.5 |
| Depends: | R (≥ 3.5.0) |
| Imports: | glmnet, randomForest, splines2, xgboost, stats, utils |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2025-08-28 |
| DOI: | 10.32614/CRAN.package.SMMAL |
| Author: | Jue Hou [aut, cre], Yuming Zhang [aut], Shuheng Kong [aut] |
| Maintainer: | Jue Hou <hou00123 at umn.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | SMMAL results |
| Reference manual: | SMMAL.html , SMMAL.pdf |
| Vignettes: |
SMMAL_vignette (source, R code) |
| Package source: | SMMAL_0.0.5.tar.gz |
| Windows binaries: | r-devel: SMMAL_0.0.5.zip, r-release: not available, r-oldrel: SMMAL_0.0.5.zip |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): SMMAL_0.0.5.tgz, r-oldrel (x86_64): SMMAL_0.0.5.tgz |
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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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