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.

翻译：增益建模是营销效果建模的基础组成部分，常用于评估处理对结果的影响。通过增益建模，我们可以识别出收益最大的处理方式，同时也能找出在特定处理下可能做出有利决策的客户。过去，增益建模方法主要依赖差分法（DID）架构，结合机器学习模型作为估计学习器，却忽略了特征间的关联与置信信息。我们提出了一种基于图神经网络的框架，将因果知识与增益值的估计相结合。首先，我们提出了基于CATE（条件平均处理效应）估计与邻接矩阵结构学习的因果表征技术。其次，我们设计了一种更可扩展的增益建模框架，基于图卷积网络来融合因果知识。研究结果表明，该方法在预测增益值时表现出良好的效果，在典型模拟数据中误差较小，其有效性在实际工业营销数据中也得到了验证。