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 methods for ATE estimation in a setting where only a treatment group and an unknown group, comprising units for which it is unclear whether they received treatment or control, are observable. This scenario is a variant of learning from positive and unlabeled data (PU learning) in weakly supervised learning and can be viewed as a special case of ATE estimation with missing data. For this setting, we derive semiparametric efficiency bounds, which are lower bounds of the asymptotic variance for regular estimators. We then propose semiparametric efficient ATE estimators that achieve these efficiency bounds in terms of their asymptotic variance. Our findings make significant contributions to causal inference with missing data and weakly supervised learning.