Weight trimming and propensity score weighting.
Weight trimming and propensity score weighting.
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DOI：
10.1371/journal.pone.0018174
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发表时间：
2011-03-31
期刊：
影响因子：
3.7
通讯作者：
Stuart EA
中科院分区：
文献类型：
--
作者：
Lee BK;Lessler J;Stuart EA
倾向分数加权对模型错误指定和可能不适当地影响结果的离群权重很敏感。采用倾向得分估计法，考察了下调较大权重是否能提高倾向得分加权的效果，以及下调的效益是否存在差异。在一项模拟研究中，作者检验了在Logistic回归、分类和回归树(CART)、增强CART和随机森林之后的权重调整的性能，以估计倾向分数权重。结果表明，尽管错误指定的Logistic回归倾向得分模型产生了更多的偏差和标准误差，但在Logistic回归之后进行权重调整可以提高最终参数估计的准确性和精确度。相比之下，减重并没有改善增强型购物车和随机林的性能。没有减重的增强型购物车和随机森林的表现与通过减重后的Logistic回归估计倾向得分所获得的最佳表现相似。虽然修剪可以用来优化通过Logistic回归估计的倾向得分权重，但修剪的最佳水平很难确定。这些结果表明，虽然修剪可以在某些设置下改进推理，但为了持续提高倾向分数加权的性能，分析师应该专注于导致权重生成的程序(即，适当指定倾向分数模型)，而不是依赖于特定的方法，如权重修剪。
Propensity score weighting is sensitive to model misspecification and outlying weights that can unduly influence results. The authors investigated whether trimming large weights downward can improve the performance of propensity score weighting and whether the benefits of trimming differ by propensity score estimation method. In a simulation study, the authors examined the performance of weight trimming following logistic regression, classification and regression trees (CART), boosted CART, and random forests to estimate propensity score weights. Results indicate that although misspecified logistic regression propensity score models yield increased bias and standard errors, weight trimming following logistic regression can improve the accuracy and precision of final parameter estimates. In contrast, weight trimming did not improve the performance of boosted CART and random forests. The performance of boosted CART and random forests without weight trimming was similar to the best performance obtainable by weight trimmed logistic regression estimated propensity scores. While trimming may be used to optimize propensity score weights estimated using logistic regression, the optimal level of trimming is difficult to determine. These results indicate that although trimming can improve inferences in some settings, in order to consistently improve the performance of propensity score weighting, analysts should focus on the procedures leading to the generation of weights (i.e., proper specification of the propensity score model) rather than relying on ad-hoc methods such as weight trimming.
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