Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge
This work addresses uplift modeling for marketing professionals by improving treatment effect estimation, though it appears incremental as it builds on existing causal and graph-based methods.
The paper tackled the problem of uplift modeling in marketing by proposing a graph neural network framework that incorporates causal knowledge, achieving small errors on simulated data and verifying effectiveness on real industry marketing data.
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.