Analyzing heterogeneous treatment effects (HTEs) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and preintervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment.…

Defining causal effects as comparisons between marginal population means, this article introduces marginal mean weighting through stratification (MMW-S) to adjust for selection bias in multilevel educational data. The article formally shows the inherent connections among the MMW-S method, propensity score stratification, and…