 Testing for arbitrary interference on experimentation platformsJ Pouget-Abadie, G Saint-Jacques, M Saveski, W Duan, S Ghosh, Y Xu and E M Airoldi Biometrika, 2019, vol. 106, issue 4, 929-940 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. Keywords: Arbitrary interference; Causal inference; Potential outcome; Violation of the stable unit treatment value assumption (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:106:y:2019:i:4:p:929-940. Ordering information: This journal article can be ordered from Access Statistics for this article Biometrika is currently edited by Paul Fearnhead More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. |
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