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背景下进行需求预测的新方法。在此场景中，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，因为零售商旨在下游决策问题中设定（利润）最优的价格。我们的方法融合了用于因果推断的双重机器学习方法论与基于Transformer的最先进预测模型。通过大量实证实验，我们一方面表明，在完全受控的合成数据（但具有现实性）环境下，我们的方法能更准确地估计因果效应；另一方面，在真实数据中，我们证明该方法在离策略场景（即定价政策发生变化时）下优于传统预测方法，而在在策略场景下仅略微落后。