Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing
Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing
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DOI：
10.1007/s10260-007-0086-0
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发表时间：
2009-07-01
影响因子：
1
通讯作者：
Mattei, Alessandra
中科院分区：
文献类型：
--
作者：
Mattei, Alessandra
倾向评分法是一种日益流行的因果推断技术。为了估计倾向评分，我们必须在给定协变量向量的情况下对治疗指标的分布进行建模。在充分观察协变量的情况下，已经做了大量的工作。不幸的是，许多大规模和复杂的调查，如纵向调查，都存在协变量值缺失的问题。在本文中，我们比较了三种不同的方法及其在倾向得分估计和使用中处理缺失背景数据的基本假设：完全案例分析，Rosenbaum和Rubin(J am Stat Asoc79：516-524,1984)提出的基于模式混合模型的方法，以及多重补偿方法。我们应用这些方法来评估生育事件对印度尼西亚个人幸福感的影响，使用来自印尼家庭生活调查的妇女样本。
Propensity score methods are an increasingly popular technique for causal inference. To estimate propensity scores, we must model the distribution of the treatment indicator given a vector of covariates. Much work has been done in the case where the covariates are fully observed. Unfortunately, many large scale and complex surveys, such as longitudinal surveys, suffer from missing covariate values. In this paper, we compare three different approaches and their underlying assumptions of handling missing background data in the estimation and use of propensity scores: a complete-case analysis, a pattern-mixture model based approach developed by Rosenbaum and Rubin (J Am Stat Assoc79:516-524, 1984), and a multiple imputation approach. We apply these methods to assess the impact of childbearing events on individuals' wellbeing in Indonesia, using a sample of women from the Indonesia Family Life Survey.