Policy-Aware Design of Large-Scale Factorial Experiments
面向政策选择的大规模析因实验设计AI 解读 ↓
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面向政策选择的大规模析因实验设计
- 研究问题
- 共享用户群上的产品实验存在组合与交互时，如何在固定预算下选出表现较好的政策，而不必估计全部处理效应？
- 方法与思路
- 作者以低秩张量表示组合的预期结果，先抽样、补全并筛除较弱因子水平，再用序贯减半从剩余组合中选择政策。
- 材料陈述的贡献
- 摘要称，方法具有简单遗憾界与识别保证，复杂度取决于低秩自由度和水平间分离结构，并在淘宝离线评价中表现较好。
- 阅读关注点
- 应核对低秩与抽样条件、筛除错误的控制，以及理论预算分配和离线评价如何对应真实平台实验中的政策价值。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未核实；摘要未列出保证的完整条件，离线优势不等于在线因果验证。
原文摘要
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 single factorial problem and models expected outcomes as a low-rank tensor. In the first stage, the platform samples a subset of intervention combinations, uses tensor completion to infer performance on untested combinations, and eliminates weak factor levels using estimated marginal contributions. In the second stage, it applies sequential halving to the surviving combinations to select a final policy. We establish gap-independent simple-regret bounds and gap-dependent identification guarantees showing that the relevant complexity scales with the degrees of freedom of the low-rank tensor and the separation structure across factor levels, rather than the full factorial size. In an offline evaluation based on a product-bundling problem constructed from 100 million Taobao interactions, the proposed method substantially outperforms one-shot tensor completion and unstructured best-arm benchmarks, especially in low-budget and high-noise settings. These results show how centralized, policy-aware experimentation can make combinatorial product design operationally feasible at platform scale.