  Uplift modeling is aimed at estimating the incremental impact of an action on
an individual's behavior, which is useful in various application domains such
as targeted marketing (advertisement campaigns) and personalized medicine
(medical treatments). Conventional methods of uplift modeling require every
instance to be jointly equipped with two types of labels: the taken action and
its outcome. However, obtaining two labels for each instance at the same time
is difficult or expensive in many real-world problems. In this paper, we
propose a novel method of uplift modeling that is applicable to a more
practical setting where only one type of labels is available for each instance.
We show a mean squared error bound for the proposed estimator and demonstrate
its effectiveness through experiments.
