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.

Demand forecasting in the online fashion industry is particularly amendable to global, data-driven forecasting models because of the industry's set of particular challenges. These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption. While standard deep learning forecasting approaches cater for many of these, the fixed inventory assumption requires a special treatment via controlling the relationship between price and demand closely. In this case study, we describe the data and our modelling approach for this forecasting problem in detail and present empirical results that highlight the effectiveness of our approach.

Importantly, our approach does not make Here, we propose a general method for probabilistic any a-priori assumptions on the underlying distribution of time series forecasting. We combine an our data. The probabilistic output of our model is generated autoregressive recurrent neural network to model via Implicit Quantile Networks (Dabney et al., 2018) temporal dynamics with Implicit Quantile Networks (IQN) and is trained by minimizing the integrand of the to learn a large class of distributions over a Continuous Ranked Probability Score (CRPS) (Matheson & time-series target. When compared to other probabilistic Winkler, 1976).