Marginal Mean Weighting Through Stratification: Adjustment for Selection Bias in Multilevel Data
Marginal Mean Weighting Through Stratification: Adjustment for Selection Bias in Multilevel Data
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DOI：
10.3102/1076998609359785
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发表时间：
2010-10-01
影响因子：
2.4
通讯作者：
Hong, Guanglei
中科院分区：
文献类型：
--
作者：
Hong, Guanglei
定义因果效应为边际人口平均值之间的比较，本文介绍了通过分层的边际平均加权（MMW-S），以调整多层次教育数据中的选择偏差。本文正式显示MMW-S方法，倾向评分分层，和治疗的逆概率加权（IPTW）之间的内在联系。MMW-S和IPTW均适用于评价多种并行治疗，因此比倾向评分的匹配、分层或协方差调整具有更广泛的应用。此外，数学上的考虑和一系列的模拟表明，MMW-S方法已经纳入了一些重要的优势，倾向得分分层方法，这通常提高了稳健性的MMW-S估计相比，IPTW估计。为了说明这一点，作者将MMW-S方法应用于小学早期阅读教学的班级内同质分组评价。
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 inverse-probability-of-treatment weighting (IPTW). Both MMW-S and IPTW are suitable for evaluating multiple concurrent treatments and hence have broader applications than matching, stratification, or covariance adjustment for the propensity score. Furthermore, mathematical consideration and a series of simulations reveal that the MMW-S method has incorporated some important strengths of the propensity score stratification method, which generally enhance the robustness of MMW-S estimates in comparison with IPTW estimates. To illustrate, the author applies the MMW-S method to evaluations of within-class homogeneous grouping in early elementary reading instruction.