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
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

Documentation:

Reference manual:	SMMAL.html , SMMAL.pdf
Vignettes:	SMMAL_vignette (source, R code)

Downloads:

Package source:	SMMAL_0.0.5.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
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

Linking:

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