PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
PUATE:处理组与未标记个体的高效平均处理效应估计
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…