Online Experimental Design With Estimation-Regret Trade-off Under Network Interference

Zhiheng Zhang, Zichen Wang

Keywords: Online experimental design, Causal inference, Network interference, Multi-armed bandits

TL;DR: Online Experimental Design With Estimation-Regret Trade-off Under Network Interference

Abstract:

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 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 to different groups and compare their outcomes. Although effective in offline
settings, 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 the
definition of arm space using the statistical concept of exposure mapping, which allows for
a 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 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 in different learning settings and network topology.

Primary Area: Probabilistic methods (e.g., variational inference, causal inference, Gaussian processes)

Submission Number: 6350