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arXiv · 2411.08352

Imputation-based randomization tests for randomized experiments with interference

Tingxuan Han·Ke Zhu·Hanzhong Liu·Ke Deng
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Abstract

The presence of interference renders classic Fisher randomization tests infeasible due to nuisance unknowns. To address this issue, we propose imputing the nuisance unknowns and computing Fisher randomization p-values multiple times, then averaging them. We term this approach the imputation-based randomization test and provide theoretical results on its asymptotic validity. Our method leverages the merits of randomization and the flexibility of the Bayesian framework: for multiple imputations, we can either employ the empirical distribution of observed outcomes to achieve robustness against model mis-specification or utilize a parametric model to incorporate prior information. Simulation results demonstrate that our method effectively controls the type I error rate and significantly enhances the testing power compared to existing randomization tests for randomized experiments with interference. We apply our method to a two-round randomized experiment with multiple treatments and one-way interference, where existing randomization tests exhibit limited power.

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Tingxuan Han, Ke Zhu, Hanzhong Liu, Ke Deng. 2024-11-13. Imputation-based randomization tests for randomized experiments with interference. https://arxiv.org/abs/2411.08352

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