IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v43y2024i3p590-614.html
   My bibliography  Save this article

A Bias Correction Approach for Interference in Ranking Experiments

Author

Listed:
  • Ali Goli

    (University of Washington, Seattle, Washington 98195)

  • Anja Lambrecht

    (London Business School, London NW1 4SA, United Kingdom)

  • Hema Yoganarasimhan

    (University of Washington, Seattle, Washington 98195)

Abstract

Online marketplaces use ranking algorithms to determine the rank-ordering of items sold on their websites. The standard practice is to determine the optimal algorithm using A/B tests. We present a theoretical framework to characterize the total average treatment effect (TATE) of a ranking algorithm in an A/B test and show that naive TATE estimates can be biased because of interference. We propose a bias-correction approach that can recover the TATE of a ranking algorithm based on past A/B tests even if those tests suffer from a combination of interference issues. Our solution leverages data across multiple experiments and identifies observations in partial equilibrium in each experiment, that is, items close to their positions under the true counterfactual equilibrium of interest. We apply our framework to data from a travel website and present comprehensive evidence for interference bias in this setting. Next, we use our solution concept to build a customized deep learning model to predict the true TATE of the main algorithm of interest in our data. Counterfactual estimates from our model show that naive TATE estimates of click and booking rates can be biased by as much as 15% and 29%, respectively.

Suggested Citation

  • Ali Goli & Anja Lambrecht & Hema Yoganarasimhan, 2024. "A Bias Correction Approach for Interference in Ranking Experiments," Marketing Science, INFORMS, vol. 43(3), pages 590-614, May.
    • Handle: RePEc:inm:ormksc:v:43:y:2024:i:3:p:590-614
    DOI: 10.1287/mksc.2022.0046
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2022.0046
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2022.0046?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David Holtz & Sinan Aral, 2020. "Limiting Bias from Test-Control Interference in Online Marketplace Experiments," Papers 2004.12162, arXiv.org.
    2. Eckles Dean & Karrer Brian & Ugander Johan, 2017. "Design and Analysis of Experiments in Networks: Reducing Bias from Interference," Journal of Causal Inference, De Gruyter, vol. 5(1), pages 1-23, March.
    3. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2014. "Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue," Management Science, INFORMS, vol. 60(7), pages 1632-1654, July.
    4. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
    5. Jia Liu & Olivier Toubia & Shawndra Hill, 2021. "Content-Based Model of Web Search Behavior: An Application to TV Show Search," Management Science, INFORMS, vol. 67(10), pages 6378-6398, October.
    6. Paramveer S. Dhillon & Sinan Aral, 2021. "Modeling Dynamic User Interests: A Neural Matrix Factorization Approach," Marketing Science, INFORMS, vol. 40(6), pages 1059-1080, November.
    7. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    8. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    9. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
    10. Hema Yoganarasimhan, 2020. "Search Personalization Using Machine Learning," Management Science, INFORMS, vol. 66(3), pages 1045-1070, March.
    11. David Holtz & Ruben Lobel & Inessa Liskovich & Sinan Aral, 2020. "Reducing Interference Bias in Online Marketplace Pricing Experiments," Papers 2004.12489, arXiv.org.
    12. Omid Rafieian & Hema Yoganarasimhan, 2021. "Targeting and Privacy in Mobile Advertising," Marketing Science, INFORMS, vol. 40(2), pages 193-218, March.
    13. Johari, Ramesh & Li, Hannah & Weintraub, Gabriel, 2020. "Experimental Design in Two-Sided Platforms: An Analysis of Bias," Research Papers 3859, Stanford University, Graduate School of Business.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Iavor Bojinov & David Simchi-Levi & Jinglong Zhao, 2023. "Design and Analysis of Switchback Experiments," Management Science, INFORMS, vol. 69(7), pages 3759-3777, July.
    2. Hannah Li & Geng Zhao & Ramesh Johari & Gabriel Y. Weintraub, 2021. "Interference, Bias, and Variance in Two-Sided Marketplace Experimentation: Guidance for Platforms," Papers 2104.12222, arXiv.org.
    3. Jinglong Zhao, 2024. "Experimental Design For Causal Inference Through An Optimization Lens," Papers 2408.09607, arXiv.org, revised Aug 2024.
    4. 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.
    5. Honka, Elisabeth & Seiler, Stephan & Ursu, Raluca, 2024. "Consumer search: What can we learn from pre-purchase data?," Journal of Retailing, Elsevier, vol. 100(1), pages 114-129.
    6. Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," SciencePo Working papers Main hal-03455978, HAL.
    7. Davide Viviano, 2020. "Experimental Design under Network Interference," Papers 2003.08421, arXiv.org, revised Jul 2022.
    8. Hema Yoganarasimhan & Ebrahim Barzegary & Abhishek Pani, 2023. "Design and Evaluation of Optimal Free Trials," Management Science, INFORMS, vol. 69(6), pages 3220-3240, June.
    9. Michael P. Leung, 2021. "Rate-Optimal Cluster-Randomized Designs for Spatial Interference," Papers 2111.04219, arXiv.org, revised Sep 2022.
    10. Hema Yoganarasimhan, 2020. "Search Personalization Using Machine Learning," Management Science, INFORMS, vol. 66(3), pages 1045-1070, March.
    11. Guillaume W Basse & Edoardo M Airoldi, 2018. "Model-assisted design of experiments in the presence of network-correlated outcomes," Biometrika, Biometrika Trust, vol. 105(4), pages 849-858.
    12. 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.
    13. Ariel Boyarsky & Hongseok Namkoong & Jean Pouget-Abadie, 2023. "Modeling Interference Using Experiment Roll-out," Papers 2305.10728, arXiv.org, revised Aug 2023.
    14. Stefan Wager & Kuang Xu, 2019. "Experimenting in Equilibrium," Papers 1903.02124, arXiv.org, revised Jun 2020.
    15. Stefan Wager & Kuang Xu, 2021. "Experimenting in Equilibrium," Management Science, INFORMS, vol. 67(11), pages 6694-6715, November.
    16. 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.
    17. Shengjun Mao & Sanjeev Dewan & Yi-Jen (Ian) Ho, 2023. "Personalized Ranking at a Mobile App Distribution Platform," Information Systems Research, INFORMS, vol. 34(3), pages 811-827, September.
    18. Dionissi Aliprantis, 2017. "Assessing the evidence on neighborhood effects from Moving to Opportunity," Empirical Economics, Springer, vol. 52(3), pages 925-954, May.
    19. Supriya Tiwari & Pallavi Basu, 2024. "Quasi-randomization tests for network interference," Papers 2403.16673, arXiv.org, revised Oct 2024.
    20. Qian, Yang & Ling, Haifeng & Meng, Xiangrui & Jiang, Yuanchun & Chai, Yidong & Liu, Yezheng, 2024. "Voice of the Professional: Acquiring competitive intelligence from large-scale professional generated contents," Journal of Business Research, Elsevier, vol. 180(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:43:y:2024:i:3:p:590-614. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.