Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
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DOI：
10.1002/sim.1903
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发表时间：
2004-10-15
影响因子：
2
通讯作者：
Davidian, M
中科院分区：
文献类型：
--
作者：
Lunceford, JK;Davidian, M
根据观察数据进行因果解释的治疗效果估计是复杂的，因为治疗暴露可能与受试者特征相混淆。倾向得分，即以协变量为条件的治疗暴露概率，是两种调整混杂的方法的基础：基于估计倾向得分的分位数对观察结果进行分层的方法和基于估计倾向得分的逆加权观察结果的方法。我们回顾了这些方法和相关方法的流行版本，提供了更高的精度，描述了理论特性并强调了它们对实践的影响，并提出了广泛的性能比较，为实际使用提供指导。版权所有：John Wiley Sons， Ltd. 2004
Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with Subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use. Copyright (C) 2004 John Wiley Sons, Ltd.