Price Experimentation and Interference

TL;DR

This paper investigates biases in price-related A/B experiments, revealing that standard estimators can mislead firms into decreasing profits, and proposes a debiasing method applicable with minimal experimental adjustments.

Contribution

It introduces a novel debiasing technique for price experiments that corrects sign errors in profit estimations using simple treatment-control splits.

Findings

Canonical estimators can have the wrong sign, leading to profit decreases.

Market factors influence the likelihood of sign errors in estimators.

Debiasing improves the reliability of price experiment results.

Abstract

In this paper, we examine the biases that arise when firms run A/B tests on continuous parameters to estimate global treatment effects on performance metrics of interest; we particularly focus on price experiments to measure the price impact on quantity demanded, and on profit. In canonical A/B experimental estimators, biases emerge due to interference between market participants. We employ structural modeling and differential calculus to derive intuitive characterizations of these biases. We then specialize our general model to the standard revenue-management pricing problem. This setting highlights a fundamental risk innate to A/B pricing experiments: that the canonical estimator for the expected change in profits, counterintuitively, can have the wrong sign in expectation. In other words, following the guidance of canonical estimators may lead firms to move prices (or fees) in the wrong direction, inadvertently decreasing profits. We introduce a novel debiasing technique for these canonical experiments, requiring only that firms equally split units between treatment and control. We apply these results to a two-sided market model, and demonstrate how the "change of sign" regime depends on market factors such as the supply/demand imbalance, and the price markup. We conclude by calibrating our revenue-management pricing model to published empirical estimates from Airbnb marketplaces, demonstrating that estimators with the wrong sign are not a knife-edge issue, and that they may be prevalent enough to be of concern to practitioners.