发表机构
Meta Platforms, Inc.（元平台公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对在线实验中的委托-代理冲突，提出将实验视为激励设计问题，并证明样本分割与收缩两种机制可分别以有限流量成本或无额外流量实现激励对齐，保障平台价值。
AI 中文摘要
评估新功能的因果效应是在线平台的核心目标。尽管近期文献通过集中式组合优化来解决测试流量有限的问题，但这一视角忽略了关键的制度现实：实验在操作上是分散的。开发新功能的实验者同时决定要测试哪些假设，且他们通常根据容易产生向上偏差的经验平均处理效应获得奖励。若不加以控制，这种委托-代理冲突会严重侵蚀平台价值，这是一种结构性失败，而传统的集中式手段（如显著性阈值和流量预算）无法解决。通过将实验重新定义为激励设计问题，我们证明两种实用机制——样本分割和收缩——能有效弥合这一差距。样本分割以有限的流量成本实现激励的完美对齐，而收缩不消耗额外流量，并保证预期效应为负的干预措施严格无利可图，从而不会被实施。
英文摘要
Evaluating the causal effect of new features is a central goal for online platforms. While recent literature addresses limited testing traffic via centralized portfolio optimization, this perspective abstracts away a critical institutional reality: experimentation is operationally decentralized. The experimenters who develop new features also dictate which hypotheses to test, and they are typically rewarded based on empirical average treatment effects that are prone to upward bias. Left unchecked, this principal-agent conflict can severely erode platform value, a structural failure that conventional centralized levers, such as significance thresholds and traffic budgets, cannot resolve. By reframing experimentation as an incentive design problem, we demonstrate that two practical mechanisms, sample splitting and shrinkage, can effectively bridge this gap. Sample splitting aligns incentives perfectly at a bounded traffic cost, while shrinkage consumes no additional traffic and guarantees that interventions with negative expected effects are strictly unprofitable to field.
