ARMA-Design：部分可观测时间序列实验中 A/B 测试的最优处理分配策略
ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments
AI总结：
针对部分可观测在线时间序列实验，本文提出受控 ARMA 模型、小信号渐近框架及约束优化与强化学习两种最优 A/B 分配算法，并通过派单模拟器和真实网约车数据验证其性能。
摘要：
在线实验被许多科技公司频繁用于评估新开发的策略、产品或处理相对于基线控制的性能。在许多应用中，实验单元会随时间接收一系列处理。为处理这些时间依赖设置，现有 A/B 测试解决方案通常假设实验环境完全可观测且满足马尔可夫条件。然而，这一假设在实践中往往并不成立。本文研究部分可观测在线实验中 A/B 测试的最优设计。我们引入受控（向量）自回归滑动平均模型，以刻画部分可观测性。我们提出小信号渐近框架，用于简化各种设计下平均处理效应估计量的渐近均方误差计算。我们开发了两种估计最优设计的算法：一种利用约束优化，另一种采用强化学习。我们通过两个真实模拟司机和乘客行为以构建虚拟环境的派单模拟器，以及来自一家网约车公司的两个真实数据集，证明了所提设计的优越性能。我们方案的 Python 实现可在 https://github.com/datake/ARMADesign 获取。
英文摘要：
Online experiments %in which experimental units receive a sequence of treatments over time are frequently employed in many technological companies to evaluate the performance of a newly developed policy, product, or treatment relative to a baseline control. In many applications, the experimental units receive a sequence of treatments over time. To handle these time-dependent settings, existing A/B testing solutions typically assume a fully observable experimental environment that satisfies the Markov condition. However, this assumption often does not hold in practice. This paper studies the optimal design for A/B testing in partially observable online experiments. We introduce a controlled (vector) autoregressive moving average model to capture partial observability. We introduce a small signal asymptotic framework to simplify the calculation of asymptotic mean squared errors of average treatment effect estimators under various designs. We develop two algorithms to estimate the optimal design: one utilizing constrained optimization and the other employing reinforcement learning. We demonstrate the superior performance of our designs using two dispatch simulators that realistically mimic the behaviors of drivers and passengers to create virtual environments, along with two real datasets from a ride-sharing company. A Python implementation of our proposal is available at https://github.com/datake/ARMADesign.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
