I never thought I'd say this but … I cheated on you, ChatGPT, and Gemini. It was just the right thing to do. But hear me out: I was paying two separate subscription fees to get help writing and generating images for my blog when I heard about this all-in-one AI tool, 1minAI. Everyone was talking about how it does everything ChatGPT and Gemini do, but more. I didn't believe it because I'd never heard of it.

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.