在仅有处理组和未知状态样本时，高效估计因果效应。
PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
- 基于正样本与未标记样本构建半参数高效估计器
- 理论证明达到最小渐近方差下界
- 适用于缺失数据与弱监督场景的因果推断
平均处理效应（ATE）是因果推断的核心问题，定义为处理组与对照组期望结果之差。本文研究仅可观测到处理组和未标记组（治疗状态未知的个体）的情形，这属于正样本与未标记数据学习（PU learning）的一种变体，也可视为缺失数据下的ATE估计特例。我们推导了该设定下的半参数效率界限，刻画了常规估计器可达到的最低渐近方差。进而构造出能够达到此界限的半参数高效ATE估计器。本研究丰富了缺失数据和弱监督学习中的因果推断理论。
原文摘要 · Abstract (English)
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.
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