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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.0.5 |
rolling linux/jammy R-4.5 | SMMAL_0.0.5.tar.gz |
1.4 MiB |
0.0.5 |
rolling linux/noble R-4.5 | SMMAL_0.0.5.tar.gz |
1.4 MiB |
0.0.5 |
rolling source/ R- | SMMAL_0.0.5.tar.gz |
1.4 MiB |
0.0.5 |
latest linux/jammy R-4.5 | SMMAL_0.0.5.tar.gz |
1.4 MiB |
0.0.5 |
latest linux/noble R-4.5 | SMMAL_0.0.5.tar.gz |
1.4 MiB |
0.0.5 |
latest source/ R- | SMMAL_0.0.5.tar.gz |
1.4 MiB |
0.0.5 |
2026-04-26 source/ R- | SMMAL_0.0.5.tar.gz |
1.4 MiB |
0.0.5 |
2026-04-23 source/ R- | SMMAL_0.0.5.tar.gz |
1.4 MiB |
0.0.5 |
2026-04-09 windows/windows R-4.5 | SMMAL_0.0.5.zip |
1.5 MiB |