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Testing for arbitrary interference on experimentation platforms

Author

Listed:
  • J Pouget-Abadie
  • G Saint-Jacques
  • M Saveski
  • W Duan
  • S Ghosh
  • Y Xu
  • E M Airoldi

Abstract

SummaryExperimentation platforms are essential to large modern technology companies, as they are used to carry out many randomized experiments daily. The classic assumption of no interference among users, under which the outcome for one user does not depend on the treatment assigned to other users, is rarely tenable on such platforms. Here, we introduce an experimental design strategy for testing whether this assumption holds. Our approach is in the spirit of the Durbin–Wu–Hausman test for endogeneity in econometrics, where multiple estimators return the same estimate if and only if the null hypothesis holds. The design that we introduce makes no assumptions on the interference model between units, nor on the network among the units, and has a sharp bound on the variance and an implied analytical bound on the Type I error rate. We discuss how to apply the proposed design strategy to large experimentation platforms, and we illustrate it in the context of an experiment on the LinkedIn platform.

Suggested Citation

  • J Pouget-Abadie & G Saint-Jacques & M Saveski & W Duan & S Ghosh & Y Xu & E M Airoldi, 2019. "Testing for arbitrary interference on experimentation platforms," Biometrika, Biometrika Trust, vol. 106(4), pages 929-940.
    • Handle: RePEc:oup:biomet:v:106:y:2019:i:4:p:929-940.
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    File URL: http://hdl.handle.net/10.1093/biomet/asz047
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    Citations

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    Cited by:

    1. Supriya Tiwari & Pallavi Basu, 2024. "Quasi-randomization tests for network interference," Papers 2403.16673, arXiv.org, revised Oct 2024.
    2. Zhaonan Qu & Ruoxuan Xiong & Jizhou Liu & Guido Imbens, 2021. "Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference," Papers 2107.12420, arXiv.org, revised Jun 2024.
    3. Han Kevin & Ugander Johan, 2023. "Model-based regression adjustment with model-free covariates for network interference," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-29, January.
    4. 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.

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