Title: Randomization tests for spillovers under general interference: A graph-theoretic approach
Authors: Panagiotis Toulis - University of Chicago (United States) [presenting]
David Puelz - University of Chicago (United States)
Avi Feller - University of California at Berkeley (United States)
Guillaume Basse - Stanford University (United States)
Abstract: Interference exists when the outcome of a unit depends on another units treatment assignment. For example, intensive policing on one street could have a spillover effect on neighboring streets. Classical randomization tests typically break down in this setting because many null hypotheses of interest are no longer sharp under interference. A promising alternative is to instead construct a conditional randomization test on a subset of units and assignments for which a given null hypothesis is sharp. Finding these subsets is challenging, however, and existing methods either have low power or are limited to special cases. We propose valid, powerful, and easy-to-implement randomization tests for a general class of null hypotheses under arbitrary interference between units. The key idea is to represent the hypothesis of interest as a bipartite graph between units and assignments and to find a biclique of this graph. Importantly, the null hypothesis is sharp for the units and assignments in this biclique, enabling randomization-based tests conditional on the biclique. We can apply off-the-shelf graph clustering methods to find such bicliques efficiently and at scale. We illustrate this approach in settings with clustered interference and show advantages over methods designed specifically for that setting. We then apply our method to a large-scale policing experiment in Medellin, Colombia, where interference has a spatial structure.