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cs.LG2025
Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
Masahiro Kato, Yuki Ikeda, Kentaro Baba +2
In this study, we propose a method for identifying potential customers in targeted marketing by applying learning from positive and unlabeled data (PU learning). We consider a scen…
cs.LG2025
PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units
Masahiro Kato, Fumiaki Kozai, Ryo Inokuchi
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. Thi…