Price Experimentation and Interference

In this paper, we examine biases arising in A/B tests where firms modify a continuous parameter, such as price, to estimate the global treatment effect of a given performance metric, such as profit. These biases emerge in canonical experimental estimators due to interference among market participants. We employ structural modeling and differential calculus to derive intuitive characterizations of these biases. We then specialize our general model to a standard revenue management pricing problem. This setting highlights a key pitfall in the use of A/B pricing experiments to guide profit maximization: notably, the canonical estimator for the expected change in profits can have the {\em wrong sign}. In other words, following the guidance of canonical estimators may lead firms to move prices in the wrong direction, inadvertently decreasing profits relative to the status quo. We introduce a novel debiasing technique for such experiments, that only requires that the firm equally split experimental units between treatment and control. We apply these results to a two-sided market model and show how this “change of sign” regime depends on model parameters such as market imbalance, as well as the price markup. We also extend our revenue management model to an alternative specification in which firms set a fixed sales fee across heterogeneous products, and show that all our results extend to this setting as well.