Estimating causal effects in e-commerce tends to involve costly treatment assignments which can be impractical in large-scale settings. Leveraging machine learning to predict such treatment effects without actual intervention is a standard practice to diminish the risk. However, existing methods for treatment effect prediction tend to rely on training sets of substantial size, which are built from real experiments and are thus inherently risky to create. In this work we propose a graph neural network to diminish the required training set size, relying on graphs that are common in e-commerce data. Specifically, we view the problem as node regression with a restricted number of labeled instances, develop a two-model neural architecture akin to previous causal effect estimators, and test varying message-passing layers for encoding. Furthermore, as an extra step, we combine the model with an acquisition function to guide the creation of the training set in settings with extremely low experimental budget. The framework is flexible since each step can be used separately with other models or treatment policies. The experiments on real large-scale networks indicate a clear advantage of our methodology over the state of the art, which in many cases performs close to random, underlining the need for models that can generalize with limited supervision to reduce experimental risks.

翻译：在电子商务中估计因果效应往往涉及成本高昂的处理分配，这在大型场景下可能不切实际。利用机器学习预测此类处理效应而无需实际干预，是降低风险的标准做法。然而，现有的处理效应预测方法通常依赖于大规模训练集，这些数据集源自真实实验，因此其构建本身具有风险。本研究提出一种图神经网络，旨在减少所需训练集规模，该方法依赖于电子商务数据中常见的图结构。具体而言，我们将该问题视为标注实例数量受限的节点回归任务，开发了一种类似于先前因果效应估计器的双模型神经架构，并测试了多种用于编码的消息传递层。此外，作为额外步骤，我们将该模型与采集函数相结合，以在实验预算极低的情况下指导训练集的构建。该框架具有灵活性，因为每个步骤均可与其他模型或处理策略单独结合使用。在真实大规模网络上的实验表明，我们的方法相较于现有技术具有明显优势——现有方法在许多情况下表现接近随机水平，这凸显了需要能够通过有限监督进行泛化的模型以降低实验风险。