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One assumption underlying the unbiasedness of global treatment effectestimates from randomized experiments is the stable unit treatment valueassumption (SUTVA). Many experiments that compare the efficacy of differentrecommendation algorithms violate SUTVA, because each algorithm is trained on apool of shared data, often coming from a mixture of recommendation algorithmsin the experiment. We explore, through simulation, cluster randomized anddata-diverted solutions to mitigating this bias, which we call "symbiosisbias."

A Study of Symbiosis Bias in A/B Tests of Recommendation Algorithms