Multilevel regression and poststratification (MRP) is a flexible modeling technique that has been used in a broad range of small-area estimation problems. Traditionally, MRP studies have been focused on non-causal settings, where estimating a single population value using a nonrepresentative sample was of primary interest. In this manuscript, MRP-style estimators will be evaluated in an experimental causal inference setting. We simulate a large-scale randomized control trial with a stratified cluster sampling design, and compare traditional and nonparametric treatment effect estimation methods with MRP methodology. Using MRP-style estimators, treatment effect estimates for areas as small as 1.3$\%$ of the population have lower bias and variance than standard causal inference methods, even in the presence of treatment effect heterogeneity. The design of our simulation studies also requires us to build upon a MRP variant that allows for non-census covariates to be incorporated into poststratification.
翻译:多层次回归和后分层(MRP)是一种灵活的建模技术,用于广泛的小地区估算问题,传统上,MRP研究侧重于非因果环境,其中对使用非代表性抽样估计单一人口价值最为关注,在这一手稿中,MRP式的估测器将在实验性因果推断中进行评估。我们模拟大规模随机控制试验,采用分层的群样抽样设计,并将传统和非参数性治疗效应估算方法与MRP方法进行比较。使用MRP式的估测器,对人口面积小至1.3美元的地区进行治疗效应估算,其偏差和差异小于标准的因果推断方法,即使存在治疗效应,也存在异性。我们模拟研究的设计也要求我们利用MRP变量,允许将非人口普查的变量纳入后分层处理。