Policy-Aware Design of Large-Scale Factorial Experiments
Digital firms routinely run many online experiments on shared user populations. When product decisions are compositional, such as combinations of interface elements, flows, messages, or incentives, the number of feasible interventions grows combinatorially, while available traffic remains limited. Overlapping experiments can therefore generate interaction effects that are poorly handled by decentralized A/B testing. We study how to design large-scale factorial experiments when the objective is not to estimate every treatment effect, but to identify a high-performing policy under a fixed experimentation budget. We propose a two-stage design that centralizes overlapping experiments into a s...
| Authors | Xin Wen, Xi Chen, Will Wei Sun et al. |
|---|---|
| Primary category | stat.ML |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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