Optimizing Randomized and Deterministic Saturation Designs under Interference

TL;DR

This paper analyzes and optimizes randomized and deterministic saturation designs for treatment assignment in the presence of interference, providing formulas and methods to improve estimator bias and variance.

Contribution

It derives closed-form bias and variance expressions under interference and introduces a deterministic saturation design to enhance estimation accuracy.

Findings

Optimal designs depend on interference structure.

Deterministic saturation can outperform randomized designs.

Simulation results demonstrate improved estimator performance.

Abstract

Randomized saturation designs are a family of designs which assign a possibly different treatment proportion to each cluster of a population at random. As a result, they generalize the well-known (stratified) completely randomized designs and the cluster-based randomized designs, which are included as special cases. We show that, under the stable unit treatment value assumption, either the cluster-based or the stratified completely randomized design are in fact optimal for the bias and variance of the difference-in-means estimator among randomized saturation designs. However, this is no longer the case when interference is present. We provide the closed form of the bias and variance of the difference-in-means estimator under a linear model of interference and investigate the optimization of each of these objectives. In addition to the randomized saturation designs, we propose a deterministic saturation design, where the treatment proportion for clusters are fixed, rather than randomized, in order to further improve the estimator under correct model specification. Through simulations, we illustrate the merits of optimizing randomized saturation designs to the graph and potential outcome structure, as well as showcasing the additional improvements yielded by well-chosen deterministic saturation designs.