Causal Forecasting for Pricing. (arXiv:2312.15282v3 [stat.ML] UPDATED)

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 …

aim arxiv context decision decision making demand demand forecasting forecasting machine machine learning making modeling novel paper price pricing profit relationship retailers set stat.ml together