Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems
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. The risk combines exposure bias, assignment-unit variance, minimum detectable effect, contamination or carryover, operational cost, and estimand mism...
| Authors | Prashant Shekhar, Caroline Howard |
|---|---|
| Primary category | stat.ML |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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