Pricing from Observational Data

Given observational data on price and demand, the price optimization problem is sometimes addressed in the literature by a predictive approach: (a) fit a model to the data that best predicts demand given price and (b) substitute the predictive model into the overall profit and optimize for price. We show that, because historical demand at all prices but the observed one is missing, the price optimization problem is not well specified by the data, and in particular, the predictive approach fails to find the optimal price. We bound the suboptimality of the predictive approach, even when the optimal price cannot be identified from the data, by leveraging the special structure of the problem. Drawing from the causal inference literature, we provide su cient conditions for the optimal price to be identifiable from the data. Given these conditions, we provide parametric and non-parametric algorithms for the price optimization problem. In the non-parametric case we prove consistency and asymptotic normality and establish rates of convergence. We develop a hypothesis test for asymptotic profit optimality of any algorithm for pricing from observational data. We use this test to demonstrate empirically in an auto loan dataset that both parametric and non-parametric predictive approaches lose significant profit relative to the optimum and that our prescriptive parametric framework leads to profit that cannot be distinguished from the optimal one, recovering 36-70% of profits lost by the predictive approaches.