Network interference has garnered significant interest in the field of causal inference. It reflects diverse sociological behaviors, wherein the treatment assigned to one individual within a network may influence the outcome of other individuals, such as their neighbors. To estimate the causal effect, one classical way is to randomly assign experimental candidates into different groups and compare their differences. However, in the context of sequential experiments, such treatment assignment may result in a large regret. In this paper, we develop a unified interference-based online experimental design framework. Compared to existing literature, we expand the definition of arm space by leveraging the statistical concept of exposure mapping. Importantly, we establish the Pareto-optimal trade-off between the estimation accuracy and regret with respect to both time period and arm space, which remains superior to the baseline even in the absence of network interference. We further propose an algorithmic implementation and model generalization.

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