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Abstract:
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In causal inference, an experiment exhibits treatment interference when the treatment status of one unit affects the response of other units. While traditional causal inference methods often assume no interference between units, there has been a recent abundance of work on the design and analysis of experiments under treatment interference—for example, those conducted on social networks. In this paper, we propose a model of treatment interference where the response of a unit depends only on its treatment status and the statuses of units within its K-neighborhood. Current methods for detecting interference include carefully designed randomized experiments and conditional randomization tests on a set of focal units. We give guidance on how to choose focal units and then we conduct a simulation study to evaluate the efficacy of existing methods for detecting arbitrary network interference under this model with this choice of focal units. We show that this choice of focal units leads to powerful tests of treatment interference which outperform experimental methods drastically.
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