Robust Estimation of Propensity Score Weights via Subclassification
Robust Estimation of Propensity Score Weights via Subclassification
复制标题
通过子分类对倾向得分权重进行稳健估计
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Xiao‐Hua Zhou
中科院分区：
文献类型：
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作者：
Linbo Wang;Yuexia Zhang;T. Richardson;Xiao‐Hua Zhou
倾向评分在从观察性研究中推断因果效应方面起着核心作用。特别是，加权和子分类是两个主要的方法来估计平均因果效应的基础上估计的倾向得分。与传统版本的倾向评分子分类估计量不同，如果正确指定倾向评分模型，则加权方法提供一致且可能有效的平均因果效应估计。然而，这种理论上的吸引力可能会减少在实践中的倾向评分模型的错误指定的敏感性。相比之下，子分类方法通常对模型误指定更鲁棒。因此，我们建议使用子分类的倾向得分权重的鲁棒估计。我们的方法是基于这样的直觉，即逆概率加权估计可以被看作是子分类估计的极限，因为子类的数量趋于无穷大。通过形式化这种直觉，我们提出了新的倾向得分加权估计，是一致的和强大的模型误设。实证研究表明，所提出的估计执行有利的现有方法相比。
The propensity score plays a central role in inferring causal effects from observational studies. In particular, weighting and subclassification are two principal approaches to estimate the average causal effect based on estimated propensity scores. Unlike the conventional version of the propensity score subclassification estimator, if the propensity score model is correctly specified, the weighting methods offer consistent and possibly efficient estimation of the average causal effect. However, this theoretical appeal may be diminished in practice by sensitivity to misspecification of the propensity score model. In contrast, subclassification methods are usually more robust to model misspecification. We hence propose to use subclassification for robust estimation of propensity score weights. Our approach is based on the intuition that the inverse probability weighting estimator can be seen as the limit of subclassification estimators as the number of subclasses goes to infinity. By formalizing this intuition, we propose novel propensity score weighting estimators that are both consistent and robust to model misspecification. Empirical studies show that the proposed estimators perform favorably compared to existing methods.
DOI：
10.1214/09-sts313
发表时间：
2010-02-01
期刊：
Statistical science : a review journal of the Institute of Mathematical Statistics
影响因子：
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作者：
Stuart EA
通讯作者：
Stuart EA