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Interference exists when a unit's outcome depends on another unit's treatmentassignment. For example, intensive policing on one street could have aspillover effect on neighboring streets. Classical randomization teststypically break down in this setting because many null hypotheses of interestare no longer sharp under interference. A promising alternative is to insteadconstruct a conditional randomization test on a subset of units and assignmentsfor which a given null hypothesis is sharp. Finding these subsets ischallenging, however, and existing methods are limited to special cases or havelimited power. In this paper, we propose valid and easy-to-implementrandomization tests for a general class of null hypotheses under arbitraryinterference between units. Our key idea is to represent the hypothesis ofinterest as a bipartite graph between units and assignments, and to find anappropriate biclique of this graph. Importantly, the null hypothesis is sharpwithin this biclique, enabling conditional randomization-based tests. We alsoconnect the size of the biclique to statistical power. Moreover, we can applyoff-the-shelf graph clustering methods to find such bicliques efficiently andat scale. We illustrate our approach in settings with clustered interferenceand show advantages over methods designed specifically for that setting. Wethen apply our method to a large-scale policing experiment in Medellin,Colombia, where interference has a spatial structure.

A Graph-Theoretic Approach to Randomization Tests of Causal Effects Under General Interference