论文标题
熵平衡连续治疗
Entropy Balancing for Continuous Treatments
论文作者
论文摘要
本文通过扩展Hainmüller（2012）的原始熵平衡方法来引入连续治疗的熵平衡（EBCT）。为了估计权重平衡，提出的方法解决了全球凸的约束优化问题。 EBCT权重可靠地消除了协变量与连续处理变量之间的Pearson相关性。即使基于广义倾向评分的其他方法往往会由于选择不足，因此在不同的治疗强度中，这种情况也是如此。此外，在避免单个单元附加的极端重量方面，优化过程更加成功。广泛的蒙特卡罗模拟表明，使用EBCT的治疗效应估计显示出相似或较低的偏差和均匀较低的均方根误差。这些特性使EBCT成为评估连续治疗的有吸引力方法。
This paper introduces entropy balancing for continuous treatments (EBCT) by extending the original entropy balancing methodology of Hainmüller (2012). In order to estimate balancing weights, the proposed approach solves a globally convex constrained optimization problem. EBCT weights reliably eradicate Pearson correlations between covariates and the continuous treatment variable. This is the case even when other methods based on the generalized propensity score tend to yield insufficient balance due to strong selection into different treatment intensities. Moreover, the optimization procedure is more successful in avoiding extreme weights attached to a single unit. Extensive Monte-Carlo simulations show that treatment effect estimates using EBCT display similar or lower bias and uniformly lower root mean squared error. These properties make EBCT an attractive method for the evaluation of continuous treatments.
