Causal Forecasting for Pricing
This addresses the problem of accurate demand forecasting for retailers to optimize pricing decisions, particularly when pricing policies change, representing an incremental advance in causal forecasting methods.
The paper tackles demand forecasting for pricing by modeling the causal effect of price on demand, using Double Machine Learning and transformers, showing improved causal estimation on synthetic data and better performance in off-policy real-world scenarios while slightly trailing in on-policy settings.
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.