Improving the Estimation of Lifetime Effects in A/B Testing via Treatment Locality
利用处理局部性改进 A/B 测试中的终身效应估计AI 解读 ↓
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利用处理局部性改进 A/B 测试中的终身效应估计
- 研究问题
- 如何利用较短期 A/B 测试观测估计持续干预的长期结果，并借助处理仅影响部分状态的局部结构提高估计效率？
- 方法与思路
- 作者在马尔可夫决策过程框架中建立推断方法与效率界，通过共享非目标状态的信息利用处理局部性，并扩展至可微估计量。
- 材料陈述的贡献
- 摘要称新估计量保持无偏，使主要方差部分获得与试验臂数相关的线性缩减，并达到考虑局部性的更紧方差下界。
- 阅读关注点
- 应核对短期观测识别终身效应的条件、局部处理的定义、跨试验臂共享信息的合法性，以及无偏性和效率界的适用范围。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及期刊接收未核验；摘要未说明动态模型条件、观测长度要求或方差缩减的具体表达式。
原文摘要
Utilizing randomized experiments to evaluate the effect of short-term treatments on the short-term outcomes has been well understood and become the golden standard in industrial practice. However, as service systems become increasingly dynamical and personalized, much focus is shifting toward maximizing long-term outcomes, such as customer lifetime value, through lifetime exposure to interventions. Our goal is to assess the impact of treatment and control policies on long-term outcomes from relatively short-term observations, such as those generated by A/B testing. A key managerial observation is that many practical treatments are local, affecting only targeted states while leaving other parts of the policy unchanged. This paper rigorously investigates whether and how such locality can be exploited to improve estimation of long-term effects in Markov Decision Processes (MDPs), a fundamental model of dynamic systems. We first develop optimal inference techniques for general A/B testing in MDPs and establish corresponding efficiency bounds. We then propose methods to harness the localized structure by sharing information on the non-targeted states. Our new estimator can achieve a linear reduction with the number of test arms for a major part of the variance without sacrificing unbiasedness. It also matches a tighter variance lower bound that accounts for locality. Furthermore, we extend our framework to a broad class of differentiable estimators, which encompasses many widely used approaches in practice. We show that all such estimators can benefit from variance reduction through information sharing without increasing their bias. Together, these results provide both theoretical foundations and practical tools for conducting efficient experiments in dynamic service systems with local treatments.