Balancing Covariates via Propensity Score Weighting
Balancing Covariates via Propensity Score Weighting
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DOI：
10.1080/01621459.2016.1260466
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发表时间：
2018-01-01
影响因子：
3.7
通讯作者：
Zaslavsky, Alan M.
中科院分区：
文献类型：
--
作者：
Li, Fan;Morgan, Kari Lock;Zaslavsky, Alan M.
协变量平衡对于无混淆的描述性或因果比较至关重要。然而，在观察性研究中，缺乏平衡是很常见的。本文考虑平衡协变量的加权策略。我们定义了一类一般的权重，即平衡权重，用来平衡治疗组间协变量的加权分布。这些权重包含倾向得分，将每个组加权到分析师选择的目标人群。该类统一了现有的加权方法，包括常用的权重，如作为特殊情况的反概率权重。推导了基于这些权重的非参数估计的一般大样本结果。我们进一步提出了一种新的权重方案，即重叠权重，其中每个单元的权重与该单元分配给相反组的概率成正比。重叠权值是有界的，并使加权平均处理效果的渐近方差在一类平衡权值中最小。重叠权值还具有理想的小样本精确平衡特性，在此基础上，我们提出了一种新的方法来实现任何选定的协变量集的均值的精确平衡。两个应用程序说明了这些方法，并将它们与其他方法进行了比较。
Covariate balance is crucial for unconfounded descriptive or causal comparisons. However, lack of balance is common in observational studies. This article considers weighting strategies for balancing covariates. We define a general class of weightsthe balancing weightsthat balance the weighted distributions of the covariates between treatment groups. These weights incorporate the propensity score to weight each group to an analyst-selected target population. This class unifies existing weighting methods, including commonly used weights such as inverse-probability weights as special cases. General large-sample results on nonparametric estimation based on these weights are derived. We further propose a new weighting scheme, the overlap weights, in which each unit's weight is proportional to the probability of that unit being assigned to the opposite group. The overlap weights are bounded, and minimize the asymptotic variance of the weighted average treatment effect among the class of balancing weights. The overlap weights also possess a desirable small-sample exact balance property, based on which we propose a new method that achieves exact balance for means of any selected set of covariates. Two applications illustrate these methods and compare them with other approaches.