  Uplift modeling is a fundamental component of marketing effect modeling,
which is commonly employed to evaluate the effects of treatments on outcomes.
Through uplift modeling, we can identify the treatment with the greatest
benefit. On the other side, we can identify clients who are likely to make
favorable decisions in response to a certain treatment. In the past, uplift
modeling approaches relied heavily on the difference-in-difference (DID)
architecture, paired with a machine learning model as the estimation learner,
while neglecting the link and confidential information between features. We
proposed a framework based on graph neural networks that combine causal
knowledge with an estimate of uplift value. Firstly, we presented a causal
representation technique based on CATE (conditional average treatment effect)
estimation and adjacency matrix structure learning. Secondly, we suggested a
more scalable uplift modeling framework based on graph convolution networks for
combining causal knowledge. Our findings demonstrate that this method works
effectively for predicting uplift values, with small errors in typical
simulated data, and its effectiveness has been verified in actual industry
marketing data.
