Exact tests for two-stage randomized designs in the presence of interference
Year: 2017
Type: preprint
Abstract: Many important causal questions address interactions between units, also known as interference, such as interactions between individuals in households, students in schools, and firms in markets. Standard methods, however, often break down in this setting. In particular, randomization tests for statistical hypotheses on treatment effects are challenging because such hypotheses are typically not sharp in the presence of interference. One approach is to conduct randomization tests conditional on a subset of units and assignments, such that the null hypothesis is sharp. While promising, existing approaches require such conditioning to be based on an inflexible partition of the space of treatment assignments, which usually leads to loss of power. In this paper, we propose a general framework for conditional randomization tests that supports more flexible conditioning, allowing us to leverage more structure from the problem and increase power. Our framework subsumes standard results in conditional randomization testing and also formalizes recent randomization tests in the presence of interference. We detail our framework for two-stage randomized designs, and illustrate our approach with an analysis of a randomized evaluation of an intervention targeting student absenteeism in the School District of Philadelphia.
Source: arXiv (Cornell University)
Authors: Guillaume Basse, Avi Feller, Panos Toulis
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2759186669