SMMAL: Semi-Supervised Estimation of Average Treatment Effects
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 + fileLICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | SMMAL results |
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