Seller-Side Experiments under Interference Induced by Feedback Loops in Two-Sided Platforms
CoRR(2024)
摘要
Two-sided platforms are central to modern commerce and content sharing and
often utilize A/B testing for developing new features. While user-side
experiments are common, seller-side experiments become crucial for specific
interventions and metrics. This paper investigates the effects of interference
caused by feedback loops on seller-side experiments in two-sided platforms,
with a particular focus on the counterfactual interleaving design, proposed in
. These feedback loops, often generated
by pacing algorithms, cause outcomes from earlier sessions to influence
subsequent ones. This paper contributes by creating a mathematical framework to
analyze this interference, theoretically estimating its impact, and conducting
empirical evaluations of the counterfactual interleaving design in real-world
scenarios. Our research shows that feedback loops can result in misleading
conclusions about the treatment effects.
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