Offline Pricing and Demand Learning with Censored Data

We study a single product pricing problem with demand censoring in an offline data-driven setting. In this problem, a retailer is given a finite amount of inventory, and faces a random demand that is price sensitive in a linear fashion with unknown price sensitivity and base demand distribution. Any unsatisfied demand that exceeds the inventory level is lost and unobservable. We assume that the retailer has access to an offline data set consisting of triples of historical price, inventory level and potentially censored sales quantity. The retailer's objective is to use the offline data set to find an optimal price, maximizing her expected revenue with finite inventories. Due to demand censoring in the offline data, we show that the existence of near-optimal algorithms in a data-driven problem -- which we call problem identifiability -- is not always guaranteed. We develop a necessary and sufficient condition for problem identifiability, and show that it is directly related to some quality metrics of the censored data. We propose a novel data-driven algorithm that hedges against the distribution uncertainty arising from censored data, and admits provable finite-sample performance guarantees regardless of problem identifiability and offline data quality. Specifically, we prove that, for identifiable problems, the proposed algorithm is near-optimal, and, for unidentifiable problems, its worst-case revenue loss approaches the best-achievable performance guarantee among all data-driven algorithms. The numerical experiments demonstrate that our algorithm is highly effective and significantly improves both the expected revenue and worst-case revenue compared with three baseline algorithms