🤖 AI Summary
This paper addresses model-free inference for dynamic causal effects in staggered-rollout cluster-randomized experiments (SR-CREs), accounting for both anticipated treatment effects and non-negligible cluster–period-level heterogeneity in size. We propose a design-based regression estimation framework that unifies three input types: individual-level data, cluster–period means, and scaled cluster–period totals. For the first time under a finite-population setting, we establish rigorous consistency, asymptotic normality, and conservativeness of the variance estimator. Theory shows that the scaled-total estimator achieves superior asymptotic efficiency. The method imposes no outcome modeling assumptions and guarantees variance conservatism in the Löwner order. By extending parallel-arm cluster-randomized experiment theory to dynamic staggered-rollout designs, our approach delivers a robust, efficient, and model-agnostic tool for causal inference in real-world policy evaluation.
📝 Abstract
Staggered rollout cluster randomized experiments (SR-CREs) are increasingly used for their practical feasibility and logistical convenience. These designs involve staggered treatment adoption across clusters, requiring analysis methods that account for an exhaustive class of dynamic causal effects, anticipation, and non-ignorable cluster-period sizes. Without imposing outcome modeling assumptions, we study regression estimators using individual data, cluster-period averages, and scaled cluster-period totals, with and without covariate adjustment from a design-based perspective, where only the treatment adoption time is random. We establish consistency and asymptotic normality of each regression estimator under a finite-population framework and formally prove that the associated variance estimators are asymptotically conservative in the Lowner ordering. Furthermore, we conduct a unified efficiency comparison of the estimators and provide practical recommendations. We highlight the efficiency advantage of using estimators based on scaled cluster-period totals with covariate adjustment over their counterparts using individual-level data and cluster-period averages. Our results rigorously justify linear regression estimators as model-assisted methods to address an entire class of dynamic causal effects in SR-CREs and significantly expand those developed for parallel-arm CREs by Su and Ding (JRSSB, 2021) to accommodate a wider class of complex experimental settings with staggered randomization.