Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
Zhiheng Zhang, Zichen Wang
Network interference has attracted significant attention in the field of causal inference, encapsulating various sociological behaviors in which the treatment assigned to one individual within a networkmay affect the outcomes of others, such as their neighbors. A key challenge in this setting is thatstandard causal inference methods often assume independent treatment effects among individuals,which may not hold in networked environments. To estimate interference-aware causal effects, atraditional approach is to inherit the independent settings, where practitioners randomly assign experimental participants to different groups and compare their outcomes. Although effective in offlinesettings, this strategy becomes problematic in sequential experiments, where suboptimal decisions persist, 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 thedefinition of arm space using the statistical concept of exposure mapping, which allows fora more flexible and context-aware representation of treatment effects in network settings. Crucially, we establish a Pareto-optimal trade-off between estimation accuracy and regret under thenetwork concerning both time period and arm space, which remains superior to baseline modelseven without network interference. Furthermore, we propose an algorithmic implementation anddiscuss its generalization in different learning settings and network topology.