Experimental Design in Two-Sided Platforms: An Analysis of Bias
研究了双边市场中实验设计的偏差问题，发现市场平衡状态决定不同随机化设计的无偏性，并提出一种双边随机化设计以降低偏差。
We develop an analytical framework to study experimental design in two-sided marketplaces. Many of these experiments exhibit interference, where an intervention applied to one market participant influences the behavior of another participant. This interference leads to biased estimates of the treatment effect of the intervention. We develop a stochastic market model and associated mean field limit to capture dynamics in such experiments and use our model to investigate how the performance of different designs and estimators is affected by marketplace interference effects. Platforms typically use two common experimental designs: demand-side “customer” randomization ([Formula: see text]) and supply-side “listing” randomization ([Formula: see text]), along with their associated estimators. We show that good experimental design depends on market balance; in highly demand-constrained markets, [Formula: see text] is unbiased, whereas [Formula: see text] is biased; conversely, in highly supply-constrained markets, [Formula: see text] is unbiased, whereas [Formula: see text] is biased. We also introduce and study a novel experimental design based on two-sided randomization ([Formula: see text]) where both customers and listings are randomized to treatment and control. We show that appropriate choices of [Formula: see text] designs can be unbiased in both extremes of market balance while yielding relatively low bias in intermediate regimes of market balance. This paper was accepted by David Simchi-Levi, revenue management and market analytics.