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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

    as
    1. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
    2. Susan Athey & Dean Eckles & Guido W. Imbens, 2018. "Exact p-Values for Network Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 230-240, January.
    3. Patrick Bajari & Brian Burdick & Guido W. Imbens & Lorenzo Masoero & James McQueen & Thomas Richardson & Ido M. Rosen, 2021. "Multiple Randomization Designs," Papers 2112.13495, arXiv.org.
    4. G W Basse & A Feller & P Toulis, 2019. "Randomization tests of causal effects under interference," Biometrika, Biometrika Trust, vol. 106(2), pages 487-494.
    5. David Holtz & Ruben Lobel & Inessa Liskovich & Sinan Aral, 2020. "Reducing Interference Bias in Online Marketplace Pricing Experiments," Papers 2004.12489, arXiv.org.
    6. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
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    Citations

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    Cited by:

    1. 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.
    2. Nathan Kallus, 2023. "Treatment Effect Risk: Bounds and Inference," Management Science, INFORMS, vol. 69(8), pages 4579-4590, August.
    3. Shan Huang & Chen Wang & Yuan Yuan & Jinglong Zhao & Jingjing Zhang, 2023. "Estimating Effects of Long-Term Treatments," Papers 2308.08152, arXiv.org.

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