Randomization Tests in Randomized Saturation Designs

Randomized saturation designs are widely used to study spillover effects in clustered populations.

In these designs, clusters are first assigned to treatment saturation levels, and units are then randomized within clusters according to the assigned saturation.

This paper develops randomization tests for such experiments under several null hypotheses that arise naturally in spillover analysis.

For a fixed pair of saturation levels, we first study two individual-level hypotheses: a partially sharp null of no spillover effect for every untreated unit and a bounded null that restricts individual spillover effects by a prespecified constant.

Both hypotheses can be tested using a common conditional randomization framework, with finite-sample validity obtained by combining the same focal-unit relabeling distribution with null-specific statistics.

We then study weak average-spillover nulls and show that, although these nulls do not yield finite-sample exact conditional tests, studentized relabeling statistics deliver asymptotically valid randomization-based inference.

Finally, for multiple ordered saturation levels, we develop a finite-sample valid unconditional pairwise-imputation test for global monotonicity of spillover effects.

Simulations and an application to the Zomba Cash Transfer experiment illustrate the finite-sample behavior and practical implementation of the methods.