Simple “random-user” A/B experiment designs fall short in the face of complex dependence structures. These can come in the form of large-scale social graphs or, more recently, spatio-temporal network interactions in a two-sided transportation marketplace. Naive designs are susceptible to statistical interference, which can lead to biased estimates of the treatment effect under study. In this talk we discuss the implications of interference for the design and analysis of live experiments at Lyft. A link is drawn between design choices and a spectrum of bias-variance tradeoffs. We also motivate the use of large-scale simulation for two purposes: as an efficient filter on candidate tests, and as a means of justifying the assumptions underlying our choice of experimental design.
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