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Faculté et Recherche

Reducing Marketplace Interference Bias via Linear Programming

25 avr
2024
15H15
Jouy-en-Josas
Anglais

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2024-04-25T15:15:00 2024-04-25T16:30:00 Reducing Marketplace Interference Bias via Linear Programming Information Systems and Operations Management  Speaker: Arthur Delarue (ISyE) Room T-004 Jouy-en-Josas

Information Systems and Operations Management 

Intervenant: Arthur Delarue (ISyE)

Salle T-004

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.

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Ajouter au calendrier
2024-04-25T15:15:00 2024-04-25T16:30:00 Reducing Marketplace Interference Bias via Linear Programming Information Systems and Operations Management  Speaker: Arthur Delarue (ISyE) Room T-004 Jouy-en-Josas