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In modern recommendation systems, the standard pipeline involves trainingmachine learning models on historical data to predict user behaviors andimprove recommendations continuously. However, these data training loops canintroduce interference in A/B tests, where data generated by control andtreatment algorithms, potentially with different distributions, are combined.To address these challenges, we introduce a novel approach called weightedtraining. This approach entails training a model to predict the probability ofeach data point appearing in either the treatment or control data andsubsequently applying weighted losses during model training. We demonstratethat this approach achieves the least variance among all estimators withoutcausing shifts in the training distributions. Through simulation studies, wedemonstrate the lower bias and variance of our approach compared to othermethods.

Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach