Randomization Inference of Heterogeneous Treatment Effects under Network Interference

Julius Owusu

Papers from arXiv.org

Abstract: We design randomization tests of heterogeneous treatment effects when units interact on a single connected network. Our modeling strategy allows network interference into the potential outcomes framework using the concept of exposure mapping. We consider a general class of null hypotheses -- representing different notions of constant and no treatment effects -- that are not sharp due to unknown parameters and multiple potential outcomes. To make the nulls sharp, we propose a conditional randomization method that expands on existing procedures. Our conditioning approach permits the use of functions of treatment as a conditioning variable, widening the scope of application of the randomization method of inference. We show that the resulting testing procedures based on our conditioning approach are valid. We demonstrate the testing methods using a network data set and also present the findings of an extensive Monte Carlo study.

Date: 2023-07, Revised 2025-01
New Economics Papers: this item is included in nep-ecm and nep-net
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