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  "Title": "Semi-Supervised Estimation of Average Treatment Effects",
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  "Description": "Provides a pipeline for estimating the average treatment\neffect via semi-supervised learning. Outcome regression is fit\nwith cross-fitting using various machine learning method or\nuser customized function. Doubly robust ATE estimation\nleverages both labeled and unlabeled data under a\nsemi-supervised missing-data framework. For more details see\nHou et al. (2021) <doi:10.48550/arxiv.2110.12336>. A detailed\nvignette is included.",
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