Causal Forecasting for Pricing

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 t...

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Bibliographic Details
Published in:arXiv.org 2024-01
Main Authors: Schultz, Douglas, Stephan, Johannes, Sieber, Julian, Yeh, Trudie, Kunz, Manuel, Doupe, Patrick, Januschowski, Tim
Format: Article
Language:English
Subjects:
Forecasting
Machine learning
Mathematical models
Pricing
Online Access:Get full text
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Summary: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.
ISSN:2331-8422