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 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 labeled samples 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干预分配，这在大规模场景下可能不切实际。利用机器学习在不进行实际干预的情况下预测此类治疗效果已成为降低风险的常规做法。然而，现有治疗效果预测方法通常依赖大规模训练集，这些训练集需通过真实实验构建，其创建本身便具有固有风险。本研究提出一种图神经网络，通过利用电子商务数据中普遍存在的图结构来缩减所需训练集规模。具体而言，我们将问题视为具有有限标记样本的节点回归任务，开发了类似于先前因果效应估计器的双模型神经架构，并测试了不同消息传递层对编码的影响。此外，作为补充步骤，我们将该模型与采集函数相结合，以指导在实验预算极低场景下的训练集构建。该框架具有灵活性，每个步骤均可单独与其他模型或策略配合使用。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，这凸显了需要能在有限标记样本下进行泛化的模型以降低实验风险。