Causal Forecasting for Pricing

• URL: http://arxiv.org/abs/2312.15282v3
• Date: Tue, 30 Jan 2024 15:53:01 GMT
• Title: Causal Forecasting for Pricing
• Authors: Douglas Schultz, Johannes Stephan, Julian Sieber, Trudie Yeh, Manuel Kunz, Patrick Doupe, Tim Januschowski
• Abstract summary: This paper proposes a novel method for demand forecasting in a pricing context. Our methods bring together the Double Machine Learning methodology for causal inference and state-of-the-art transformer-based forecasting models.
• Score: 7.077353694086149
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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