Reducing Marketplace Interference Bias Via Shadow Prices

TL;DR

This paper introduces methods to correct bias in marketplace A/B tests caused by interference, using shadow prices and optimization techniques to improve the accuracy of causal estimates in matching-driven markets.

Contribution

The paper proposes novel shadow price-based estimators that account for marketplace interference, providing unbiased and less biased alternatives to standard RCTs in matching platforms.

Findings

Shadow price comparison yields more accurate estimates.

Proposed estimators are less biased than traditional RCTs.

The methods are validated under realistic marketplace assumptions.

Abstract

Marketplace companies rely heavily on experimentation when making changes to the design or operation of their platforms. The workhorse of experimentation is the randomized controlled trial (RCT), or A/B test, in which users are randomly assigned to treatment or control groups. However, marketplace interference causes the Stable Unit Treatment Value Assumption (SUTVA) to be violated, leading to bias in the standard RCT metric. In this work, we propose techniques for platforms to run standard RCTs and still obtain meaningful estimates despite the presence of marketplace interference. We specifically consider a generalized matching setting, in which the platform explicitly matches supply with demand via a linear programming algorithm. Our first proposal is for the platform to estimate the value of global treatment and global control via optimization. We prove that this approach is unbiased in the fluid limit. Our second proposal is to compare the average shadow price of the treatment and control groups rather than the total value accrued by each group. We prove that this technique corresponds to the correct first-order approximation (in a Taylor series sense) of the value function of interest even in a finite-size system. We then use this result to prove that, under reasonable assumptions, our estimator is less biased than the RCT estimator. At the heart of our result is the idea that it is relatively easy to model interference in matching-driven marketplaces since, in such markets, the platform mediates the spillover.