SMMAL
Semi-Supervised Estimation of Average Treatment Effects
v0.0.5
·
Aug 28, 2025
·
MIT + file LICENSE
Description
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.
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0.0.5
Mar 10, 2026