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.

本文提出了一种在定价环境中进行需求预测的新方法，通过建模价格作为需求的输入变量之间的因果关系，结合双机器学习方法和基于 Transformer 的预测模型。通过广泛的实证实验，在一个完全控制的设置中，我们证明了我们的方法通过合成但真实的数据更好地估计了因果效应。同时，我们展示了在真实世界数据中，我们的方法在离线策略设置（即定价策略发生变化时）中表现优于预测方法，只在在线策略设置中略有落后。