Department of Comparative Human Development, University of Chicago, 5736 South Woodlawn Avenue, Chicago, IL 60637, USA.
Psychol Methods. 2012 Mar;17(1):44-60. doi: 10.1037/a0024918. Epub 2011 Aug 15.
Propensity score matching and stratification enable researchers to make statistical adjustment for a large number of observed covariates in nonexperimental data. These methods have recently become popular in psychological research. Yet their applications to evaluations of multi-valued and multiple treatments are limited. The inverse-probability-of-treatment weighting method, though suitable for evaluating multi-valued and multiple treatments, often generates results that are not robust when only a portion of the population provides support for causal inference or when the functional form of the propensity score model is misspecified. The marginal mean weighting through stratification (MMW-S) method promises a viable nonparametric solution to these problems. By computing weights on the basis of stratified propensity scores, MMW-S adjustment equates the pretreatment composition of multiple treatment groups under the assumption that unmeasured covariates do not confound the treatment effects given the observed covariates. Analyzing data from a weighted sample, researchers can estimate a causal effect by computing the difference between the estimated average potential outcomes associated with alternative treatments within the analysis of variance framework. After providing an intuitive illustration of the theoretical rationale underlying the weighting method for causal inferences, the article demonstrates how to apply the MMW-S method to evaluations of treatments measured on a binary, ordinal, or nominal scale approximating a completely randomized experiment; to studies of multiple concurrent treatments approximating factorial randomized designs; and to moderated treatment effects approximating randomized block designs. The analytic procedure is illustrated with an evaluation of educational services for English language learners attending kindergarten in the United States.
倾向评分匹配和分层使研究人员能够在非实验数据中对大量观察到的协变量进行统计调整。这些方法最近在心理学研究中变得很流行。然而，它们在多值和多处理的评估中的应用受到限制。尽管逆概率处理加权法适用于评估多值和多处理，但当只有一部分人群为因果推理提供支持，或者倾向评分模型的函数形式被错误指定时，它通常会产生不稳定的结果。通过分层的边际均值加权法 (MMW-S) 有望成为解决这些问题的可行非参数解决方案。通过基于分层倾向评分计算权重，MMW-S 调整假设在给定观察到的协变量的情况下，未测量的协变量不会混淆处理效果，从而使多个处理组的预处理组成相等。通过对加权样本进行分析，研究人员可以通过在方差分析框架内计算与替代处理相关的估计平均潜在结果之间的差异来估计因果效应。在提供了因果推理加权方法的理论原理的直观说明之后，本文展示了如何将 MMW-S 方法应用于评估近似完全随机实验的二进制、有序或名义尺度的处理；用于近似析因随机设计的多个并发处理的研究；以及用于近似随机区组设计的调节处理效果的研究。分析过程通过评估在美国上幼儿园的英语学习者的教育服务来说明。
