Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies
Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies
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DOI：
10.1093/pan/mpr025
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发表时间：
2012-12-01
影响因子：
5.4
通讯作者：
Hainmueller, Jens
中科院分区：
文献类型：
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作者：
Hainmueller, Jens
本文提出了熵平衡，一种数据预处理方法，以实现协变量平衡的观察性研究与二进制处理。熵平衡依赖于最大熵重新加权方案，该方案校准单位权重，使得重新加权的治疗组和对照组满足潜在的大的预先指定的平衡条件集合，该平衡条件集合包含关于已知样本矩的信息。从而，熵平衡精确地调整了协变量分布的一阶、二阶以及可能的更高阶矩的表示中的不平等。这些平衡的改善可以减少模型的依赖性，为后续的治疗效果的估计。该方法确保平衡改善了重新加权中包含的所有协变量矩。它还消除了对倾向评分模型的持续平衡检查和迭代搜索的需要，这些模型可能会随机平衡协变量矩。我们证明了使用的熵平衡与蒙特卡罗模拟和实证应用。
This paper proposes entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments. Entropy balancing relies on a maximum entropy reweighting scheme that calibrates unit weights so that the reweighted treatment and control group satisfy a potentially large set of prespecified balance conditions that incorporate information about known sample moments. Entropy balancing thereby exactly adjusts inequalities in representation with respect to the first, second, and possibly higher moments of the covariate distributions. These balance improvements can reduce model dependence for the subsequent estimation of treatment effects. The method assures that balance improves on all covariate moments included in the reweighting. It also obviates the need for continual balance checking and iterative searching over propensity score models that may stochastically balance the covariate moments. We demonstrate the use of entropy balancing with Monte Carlo simulations and empirical applications.