Causal Forecasting for Pricing
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.
Cite
@article{arxiv.2312.15282,
title = {Causal Forecasting for Pricing},
author = {Douglas Schultz and Johannes Stephan and Julian Sieber and Trudie Yeh and Manuel Kunz and Patrick Doupe and Tim Januschowski},
journal= {arXiv preprint arXiv:2312.15282},
year = {2024}
}