Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Yanagi, Takahide
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225138027