The paper derives semiparametric efficiency bounds for average treatment effect estimation from treated and unlabeled units, and proposes estimators that attain these bounds only when the propensity score and related nuisance functions are known.
Learning from positive and unlabeled data under the selected at random assumption
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PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
The paper derives semiparametric efficiency bounds for average treatment effect estimation from treated and unlabeled units, and proposes estimators that attain these bounds only when the propensity score and related nuisance functions are known.