Graph Neural Network with Two Uplift Estimators for Label-Scarcity
  Individual Uplift Modeling
Dingyuan Zhu Daixin Wang Zhiqiang Zhang Kun Kuang Yan Zhang Yulin Kang Jun Zhou
Uplift modeling aims to measure the incremental effect, which we call uplift,
of a strategy or action on the users from randomized experiments or
observational data. Most existing uplift methods only use individual data,
which are usually not informative enough to capture the unobserved and complex
hidden factors regarding the uplift. Furthermore, uplift modeling scenario
usually has scarce labeled data, especially for the treatment group, which also
poses a great challenge for model training. Considering that the neighbors'
features and the social relationships are very informative to characterize a
user's uplift, we propose a graph neural network-based framework with two
uplift estimators, called GNUM, to learn from the social graph for uplift
estimation. Specifically, we design the first estimator based on a
class-transformed target. The estimator is general for all types of outcomes,
and is able to comprehensively model the treatment and control group data
together to approach the uplift. When the outcome is discrete, we further
design the other uplift estimator based on our defined partial labels, which is
able to utilize more labeled data from both the treatment and control groups,
to further alleviate the label scarcity problem. Comprehensive experiments on a
public dataset and two industrial datasets show a superior performance of our
proposed framework over state-of-the-art methods under various evaluation
metrics. The proposed algorithms have been deployed online to serve real-world
uplift estimation scenarios.