Remote Auditing: Design-based Tests of Randomization, Selection, and Missingness with Broadly Accessible Satellite Imagery

Abstract

Randomized controlled trials (RCTs) are the benchmark for causal inference, yet field implementation can drift from the registered design or, by chance, yield imbalances. We introduce a remote audit -- a preregistrable, design-based diagnostic that uses strictly pre-treatment, publicly available satellite imagery to test whether assignment is independent of local conditions. The audit implements a conditional randomization test that asks whether treatment is more predictable from pre-treatment features than under the registered mechanism, delivering a finite-sample-valid, nonparametric check that honors blocks and clusters and controls multiplicity across image models, resolutions, and patch sizes via a max-statistic. The same preregistered procedure can be run before baseline data collection to guide implementation and, after assignments are realized, to audit the actual allocation. In two illustrations -- Uganda's Youth Opportunities Program (randomization corroborated) and a school-based experiment in Bangladesh (assignment predictable relative to the design, consistent with independent concerns) -- the audit can surface potential problems early, before costly scientific investments. We also provide descriptive diagnostics for selection into the study and for missingness. Because it is low-cost and can be implemented rapidly in a unified way across diverse global administrative jurisdictions, the remote audit complements balance tests, strengthens preregistration, and enables rapid design checks when conventional data collection is slow, expensive, or infeasible.