Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
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网络干扰下兼顾估计精度与遗憾的在线实验设计
- 研究问题
- 在个体处理会影响网络邻居的序贯实验中，如何同时估计考虑干扰的因果效应，并控制持续采取次优决策所产生的遗憾？
- 方法与思路
- 作者提出考虑干扰的在线实验框架，用暴露映射扩展行动空间，分析估计精度与遗憾的权衡，并给出算法及推广讨论。
- 材料陈述的贡献
- 摘要称建立了关于实验时长和行动空间的帕累托最优权衡，且在无网络干扰时仍优于基准，并讨论不同学习场景和网络拓扑。
- 阅读关注点
- 核对暴露映射对应的效应参数、网络信息要求、遗憾定义，以及最优性上下界和算法实现所需条件，检查基准比较是否同口径。
- 阅读边界
- 这是预印本，同行评审及接收状态未核实。摘要未说明干扰结构、最优性条件与具体界，所称优于基准不能外推至所有网络实验。
原文摘要
Network interference has attracted significant attention in the field of causal inference, encapsulating various sociological behaviors where the treatment assigned to one individual within a network may affect the outcomes of others, such as their neighbors. A key challenge in this setting is that standard causal inference methods often assume independent treatment effects among individuals, which may not hold in networked environments. To estimate interference-aware causal effects, a traditional approach is to inherit the independent settings, where practitioners randomly assign experimental participants into different groups and compare their outcomes. While effective in offline settings, this strategy becomes problematic in sequential experiments, where suboptimal decision persists, leading to substantial regret. To address this issue, we introduce a unified interference-aware framework for online experimental design. Compared to existing studies, we extend the definition of arm space by utilizing the statistical concept of exposure mapping, which allows for a more flexible and context-aware representation of treatment effects in networked settings. Crucially, we establish a Pareto-optimal trade-off between estimation accuracy and regret under the network concerning both time period and arm space, which remains superior to baseline models even without network interference. Furthermore, we propose an algorithmic implementation and discuss its generalization across different learning settings and network topology.