This paper proposes a novel method for demand forecasting in a pricing context. Here, modeling the causal relationship between price as an input variable to demand is crucial because retailers aim to set prices in a (profit) optimal manner in a downstream decision making problem. Our methods bring together the Double Machine Learning methodology for causal inference and state-of-the-art transformer-based forecasting models. In extensive empirical experiments, we show on the one hand that our method estimates the causal effect better in a fully controlled setting via synthetic, yet realistic data. On the other hand, we demonstrate on real-world data that our method outperforms forecasting methods in off-policy settings (i.e., when there's a change in the pricing policy) while only slightly trailing in the on-policy setting.

翻译：本文提出了一种在定价场景下进行需求预测的新方法。在此类问题中，将价格作为输入变量与需求之间的因果关系进行建模至关重要，因为零售商的目标是在下游决策问题中实现（利润）最优定价。我们的方法将用于因果推断的双重机器学习框架与基于Transformer的最先进预测模型相结合。通过大规模实证实验，一方面，我们利用合成（但基于真实场景）的数据在全控制环境下验证了该方法能更准确地估计因果效应。另一方面，我们在真实数据上证明，该方法在离策略场景（即定价策略发生变化时）中优于传统预测方法，仅在策略场景中略逊一筹。