Propensity score analysis methods with balancing constraints: A Monte Carlo study.
Propensity score analysis methods with balancing constraints: A Monte Carlo study.
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DOI：
10.1177/0962280220983512
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发表时间：
2021-04
影响因子：
2.3
通讯作者：
Li, Liang
中科院分区：
文献类型：
--
作者：
Li, Yan;Li, Liang
关键词：
逆概率加权是估计平均治疗效果的重要倾向评分加权方法。最近的文献表明，它可以很容易地与协变量平衡约束相结合，以减少过大的权重和改善平衡的不利影响。其他方法可用于推导权重，以平衡治疗组之间的协变量分布，而不涉及倾向评分。我们进行了全面的蒙特卡罗实验，研究是否使用协变量平衡约束规避需要正确的倾向得分模型规范，以及是否使用倾向得分模型进一步提高估计性能的方法，使用类似的协变量平衡约束。我们比较了简单的逆概率加权，两个倾向得分加权方法与平衡约束（协变量平衡倾向得分，协变量平衡评分规则），和两个加权方法与平衡约束，但没有使用倾向得分（熵平衡和内核平衡）。我们观察到，正确规范的倾向评分模型仍然很重要，即使当约束有效地平衡协变量。我们还观察到的证据表明，在类似的协变量平衡约束下，当协变量的维数很大时，使用倾向评分模型可以提高估计性能。这些发现表明，重要的是要开发灵活的数据驱动的倾向评分模型，满足协变量平衡条件。
The inverse probability weighting is an important propensity score weighting method to estimate the average treatment effect. Recent literature shows that it can be easily combined with covariate balancing constraints to reduce the detrimental effects of excessively large weights and improve balance. Other methods are available to derive weights that balance covariate distributions between the treatment groups without the involvement of propensity scores. We conducted comprehensive Monte Carlo experiments to study whether the use of covariate balancing constraints circumvent the need for correct propensity score model specification, and whether the use of a propensity score model further improves the estimation performance among methods that use similar covariate balancing constraints. We compared simple inverse probability weighting, two propensity score weighting methods with balancing constraints (covariate balancing propensity score, covariate balancing scoring rule), and two weighting methods with balancing constraints but without using the propensity scores (entropy balancing and kernel balancing). We observed that correct specification of the propensity score model remains important even when the constraints effectively balance the covariates. We also observed evidence suggesting that, with similar covariate balance constraints, the use of a propensity score model improves the estimation performance when the dimension of covariates is large. These findings suggest that it is important to develop flexible data-driven propensity score models that satisfy covariate balancing conditions.
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影响因子：
5.4
作者：
Hainmueller, Jens
通讯作者：
Hainmueller, Jens
DOI：
10.1111/rssb.12027
发表时间：
2014-01-01
影响因子：
5.8
作者：
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通讯作者：
Ratkovic, Marc
影响因子：
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作者：
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通讯作者：
Berk, Richard A.
DOI：
10.1080/01621459.2016.1260466
发表时间：
2018-01-01
影响因子：
3.7
作者：
Li, Fan;Morgan, Kari Lock;Zaslavsky, Alan M.
通讯作者：
Zaslavsky, Alan M.
DOI：
10.1111/rssb.12129
发表时间：
2016-06
期刊：
Journal of the Royal Statistical Society. Series B, Statistical methodology
影响因子：
--
作者：
Chan KC;Yam SC;Zhang Z
通讯作者：
Zhang Z