Selective Randomization Inference for Interference

Takahide Yanagi

Papers from arXiv.org

Abstract: Empirical researchers often use tests for interference as pretests or robustness checks. However, when the results guide the choice of models, parameters, or hypotheses, the resulting data-dependent selection can compromise the validity of subsequent inference. This paper develops a two-stage selective randomization inference framework for interference that explicitly conditions on the selection event to account for data-dependent selection. We first test the null hypothesis of no treatment spillover using a conditional randomization method. If this null is rejected, we test the null hypothesis of zero exposure contrast under a pre-specified exposure mapping that allows for spillover effects; otherwise, we test the null hypothesis of no direct effects under the assumption of no spillover. We prove that each second-stage test controls the selective Type I error rate in finite samples and evaluate the size and power of the proposed tests through Monte Carlo experiments.

Date: 2026-11
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