The performance of estimators based on the propensity score
The performance of estimators based on the propensity score
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DOI：
10.1016/j.jeconom.2012.11.006
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发表时间：
2013-07-01
影响因子：
6.3
通讯作者：
Wunsch, Conny
中科院分区：
文献类型：
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作者：
Huber, Martin;Lechner, Michael;Wunsch, Conny
我们研究了大量估计量的有限样本属性，以估计被治疗者的平均治疗效果，这些属性适合需要对观察到的协变量进行调整时，例如逆概率加权、核和其他匹配变体，以及不同的参数模型。所使用的模拟设计基于通常用于评估德国劳动力市场计划的真实数据。我们改变了设计的几个具有实际重要性的维度，例如样本大小、结果变量的类型以及选择过程的各个方面。我们发现，用过多的权重来修剪个体观测值以及调整参数的选择对于所有估计器来说都很重要。我们的模拟得出的结论是，特定的半径匹配估计器与回归相结合，总体表现最佳，特别是当对倾向得分和不同类型的结果变量的错误指定的鲁棒性被认为是一个重要属性时。 (C) 2013 Elsevier B.V. 保留所有权利。
We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observed covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design that are of practical importance, like sample size, the type of the outcome variable, and aspects of the selection process. We find that trimming individual observations with too much weight as well as the choice of tuning parameters are important for all estimators. A conclusion from our simulations is that a particular radius matching estimator combined with regression performs best overall, in particular when robustness to misspecifications of the propensity score and different types of outcome variables is considered an important property. (C) 2013 Elsevier B.V. All rights reserved.