Challenges With Propensity Score Strategies in a High-Dimensional Setting and a Potential Alternative
Challenges With Propensity Score Strategies in a High-Dimensional Setting and a Potential Alternative
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DOI：
10.1080/00273171.2011.570161
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发表时间：
2011-01-01
影响因子：
3.8
通讯作者：
Zhai, Fuhua
中科院分区：
文献类型：
--
作者：
Hill, Jennifer;Weiss, Christopher;Zhai, Fuhua
本文探讨了在尝试使用具有大量协变量的数据实现倾向评分策略来回答因果问题时出现的一些挑战。我们讨论了倾向得分估计策略、匹配和加权实施策略、平衡诊断和最终分析模型的选择。我们展示了这些选择的不同组合可能产生的广泛估计范围。最后，提出了一种在简单性和可靠性方面可能具有优势的替代估计策略。这些问题在一个实证例子的背景下进行了探讨，该实证例子使用了来自幼儿纵向研究的数据，幼儿园队列，以调查一年级后年级保留对随后认知结果的潜在影响。
This article explores some of the challenges that arise when trying to implement propensity score strategies to answer a causal question using data with a large number of covariates. We discuss choices in propensity score estimation strategies, matching and weighting implementation strategies, balance diagnostics, and final analysis models. We demonstrate the wide range of estimates that can result from different combinations of these choices. Finally, an alternative estimation strategy is presented that may have benefits in terms of simplicity and reliability. These issues are explored in the context of an empirical example that uses data from the Early Childhood Longitudinal Study, Kindergarten Cohort to investigate the potential effect of grade retention after the 1st-grade year on subsequent cognitive outcomes.