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

TL;DR

This paper introduces a novel online experimental design framework that balances estimation accuracy and regret in networked environments with interference, extending traditional methods to sequential settings.

Contribution

It proposes a unified interference-aware framework using exposure mapping and establishes a Pareto-optimal trade-off between estimation and regret in network experiments.

Findings

Achieves a Pareto-optimal trade-off between estimation accuracy and regret.

Extends arm space definition with exposure mapping for better context-awareness.

Demonstrates superior performance over baseline models even without interference.

Abstract

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.