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Experimental Design in Two-Sided Platforms: An Analysis of Bias

Author

Listed:
  • Ramesh Johari

    (Management Science and Engineering, Stanford University, Stanford, California 94305)

  • Hannah Li

    (Management Science and Engineering, Stanford University, Stanford, California 94305)

  • Inessa Liskovich

    (Airbnb Inc., San Francisco, California 94117)

  • Gabriel Y. Weintraub

    (Graduate School of Business, Stanford University, Stanford, California 94305)

Abstract

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 ( CR ) and supply-side “listing” randomization ( LR ), along with their associated estimators. We show that good experimental design depends on market balance; in highly demand-constrained markets, CR is unbiased, whereas LR is biased; conversely, in highly supply-constrained markets, LR is unbiased, whereas CR is biased. We also introduce and study a novel experimental design based on two-sided randomization ( TSR ) where both customers and listings are randomized to treatment and control. We show that appropriate choices of TSR designs can be unbiased in both extremes of market balance while yielding relatively low bias in intermediate regimes of market balance.

Suggested Citation

  • Ramesh Johari & Hannah Li & Inessa Liskovich & Gabriel Y. Weintraub, 2022. "Experimental Design in Two-Sided Platforms: An Analysis of Bias," Management Science, INFORMS, vol. 68(10), pages 7069-7089, October.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:10:p:7069-7089
    DOI: 10.1287/mnsc.2021.4247
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Ruohan Zhan & Shichao Han & Yuchen Hu & Zhenling Jiang, 2024. "Estimating Treatment Effects under Recommender Interference: A Structured Neural Networks Approach," Papers 2406.14380, arXiv.org, revised Jul 2024.
    3. Evan Munro & David Jones & Jennifer Brennan & Roland Nelet & Vahab Mirrokni & Jean Pouget-Abadie, 2023. "Causal Estimation of User Learning in Personalized Systems," Papers 2306.00485, arXiv.org.
    4. Luofeng Liao & Christian Kroer, 2024. "Statistical Inference and A/B Testing in Fisher Markets and Paced Auctions," Papers 2406.15522, arXiv.org, revised Aug 2024.
    5. Nathan Kallus, 2023. "Treatment Effect Risk: Bounds and Inference," Management Science, INFORMS, vol. 69(8), pages 4579-4590, August.
    6. Shan Huang & Chen Wang & Yuan Yuan & Jinglong Zhao & Jingjing Zhang, 2023. "Estimating Effects of Long-Term Treatments," Papers 2308.08152, arXiv.org.
    7. Shuze Chen & David Simchi-Levi & Chonghuan Wang, 2024. "Experimenting on Markov Decision Processes with Local Treatments," Papers 2407.19618, arXiv.org.
    8. Steven Wilkins Reeves & Shane Lubold & Arun G. Chandrasekhar & Tyler H. McCormick, 2024. "Model-Based Inference and Experimental Design for Interference Using Partial Network Data," Papers 2406.11940, arXiv.org.
    9. Ozan Candogan & Chen Chen & Rad Niazadeh, 2024. "Correlated Cluster-Based Randomized Experiments: Robust Variance Minimization," Management Science, INFORMS, vol. 70(6), pages 4069-4086, June.
    10. Jinglong Zhao, 2024. "Experimental Design For Causal Inference Through An Optimization Lens," Papers 2408.09607, arXiv.org, revised Aug 2024.

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