Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems
arXiv:2605.25290v1 Announce Type: cross Abstract: Online experiments in ads, recommendation, and member-experience systems are often planned before the dominant interference mechanism is known. A treatment may propagate through budgets, inventory, producer exposure, graph spillovers, or temporal carryover, making the randomization design itself a statistical decision. We formulate this problem as robust design selection over uncertain exposure mechanisms. Given a finite catalog of six implementable designs, the selector compares each design by worst-case planning risk over an ambiguity set. Th
The increasing complexity of online platforms and the drive for more efficient experimentation necessitate advanced methods to account for interference, which is a growing problem as systems become more interconnected.
This research provides a framework for optimizing online experiments in complex AI-driven systems, leading to more reliable insights and better decision-making for product development and user experience.
The ability to select optimal experimental designs proactively under uncertainty about interference mechanisms will improve the validity and efficiency of A/B testing in large-scale online systems.
- · Large online platforms
- · AI/ML researchers
- · Digital advertising industry
- · E-commerce companies
- · Companies relying on naive A/B testing
- · Inefficient experimentation methodologies
More accurate measurement of intervention effects in online systems leads to improved product features and algorithms.
Enhanced efficiency in experimentation reduces development cycles and speeds up innovation in consumer-facing AI applications.
Sophisticated experimentation capabilities become a competitive advantage, driving further consolidation or specialization among platforms able to implement these methods effectively.
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Read at arXiv cs.LG