  Uplift modeling is widely used in performance marketing to estimate effects
of promotion campaigns (e.g., increase of customer retention rate). Since it is
impossible to observe outcomes of a recipient in treatment (e.g., receiving a
certain promotion) and control (e.g., without promotion) groups simultaneously
(i.e., counter-factual), uplift models are mainly trained on instances of
treatment and control groups separately to form two models respectively, and
uplifts are predicted by the difference of predictions from these two models
(i.e., two-model method). When responses are noisy and the treatment effect is
fractional, induced individual uplift predictions will be inaccurate, resulting
in targeting undesirable customers. Though it is impossible to obtain the ideal
ground-truth individual uplifts, known as Individual Treatment Effects (ITEs),
alternatively, an average uplift of a group of users, called Average Treatment
Effect (ATE), can be observed from experimental deliveries. Upon this, similar
to Multiple Instance Learning (MIL) in which each training sample is a bag of
instances, our framework sums up individual user uplift predictions for each
bag of users as its bag-wise ATE prediction, and regularizes it to its ATE
label, thus learning more accurate individual uplifts. Additionally, to amplify
the fractional treatment effect, bags are composed of instances with adjacent
individual uplift predictions, instead of random instances. Experiments
conducted on two datasets show the effectiveness and universality of the
proposed framework.
