2023/12/23 by Douglas H. Schultz, Schultz, Douglas, Johannes Stephan +11 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Monetary Policy and Economic Impact #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2312.15282
openalex publication_date 2023/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.