A simple and successful shrinkage method for weighting estimators of treatment effects
A simple and successful shrinkage method for weighting estimators of treatment effects
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一种简单而成功的收缩方法，用于治疗效果的加权估计
DOI：
10.1016/j.csda.2014.09.015
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
S. D. Uysal
中科院分区：
文献类型：
--
作者：
W. Pohlmeier;Ruben Seiberlich;S. D. Uysal
提出了一种简单的收缩方法，以改善加权估计的平均处理效果的性能。由于在这些估计的权重可以成为任意大的倾向分数接近的边界，三个不同的变体的倾向分数的收缩方法进行了分析。一个全面的Monte Carlo研究的结果表明，这种简单的方法大大降低了估计的均方误差在有限的样本，是上级优于几个流行的修剪方法在广泛的设置。
A simple shrinkage method is proposed to improve the performance of weighting estimators of the average treatment effect. As the weights in these estimators can become arbitrarily large for the propensity scores close to the boundaries, three different variants of a shrinkage method for the propensity scores are analyzed. The results of a comprehensive Monte Carlo study demonstrate that this simple method substantially reduces the mean squared error of the estimators in finite samples, and is superior to several popular trimming approaches over a wide range of settings.