标题： PUATE：从处理（阳性）和未标记单元中有效估计平均治疗效应 显示英文标题

标题： PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units

摘要： 平均治疗效果（ATE）的估计，定义为治疗组和对照组预期结果之间的差异，是因果推断中的一个核心话题。 本研究在仅观察到治疗组和一个未标记组（由其治疗状态未知的单元组成）的情况下，开发了ATE的半参数有效估计量。 这种场景构成了从正样本和未标记样本（PU学习）中学习的一种变体，并且可以被视为带有缺失数据的ATE估计的一个特例。 对于这种情况，我们得出了半参数效率边界，这些边界刻画了规则估计量可达到的最低渐近方差。 然后，我们构建了达到这些边界的半参数有效ATE估计量。 我们的结果有助于缺失数据和弱监督学习的因果推断文献。 显示英文摘要

摘要： The estimation of average treatment effects (ATEs), defined as the difference in expected outcomes between treatment and control groups, is a central topic in causal inference. This study develops semiparametric efficient estimators for ATE in a setting where only a treatment group and an unlabeled group, consisting of units whose treatment status is unknown, are observed. This scenario constitutes a variant of learning from positive and unlabeled data (PU learning) and can be viewed as a special case of ATE estimation with missing data. For this setting, we derive the semiparametric efficiency bounds, which characterize the lowest achievable asymptotic variance for regular estimators. We then construct semiparametric efficient ATE estimators that attain these bounds. Our results contribute to the literature on causal inference with missing data and weakly supervised learning.