论文标题
平均因果效应的熵平衡估计值的大样本特性
Large Sample Properties of Entropy Balancing Estimators of Average Causal Effects
论文作者
论文摘要
加权方法用于观察研究中，以调整治疗组和对照组之间的协变量失衡。熵平衡（EB）是具有估计倾向评分的反概率加权的替代方法。 EB重量的构建是为了满足平衡约束，并针对稳定性进行了优化。我们根据Kullback-Leibler和二次rényi相对熵描述了平均因果治疗效应的EB估计值的大样本特性。此外，我们提出了其渐近方差的估计值。即使EB的目的是减少模型依赖性，估计器通常不一致，除非满足倾向得分或条件结果的隐式参数假设。通过仿真研究研究了估计量的有限样品特性。在具有瑞典儿童糖尿病登记册的观察数据的应用中，我们估计由于1型糖尿病急性并发症而导致的学校成就对住院的平均影响。
Weighting methods are used in observational studies to adjust for covariate imbalances between treatment and control groups. Entropy balancing (EB) is an alternative to inverse probability weighting with an estimated propensity score. The EB weights are constructed to satisfy balance constraints and optimized towards stability. We describe large sample properties of EB estimators of the average causal treatment effect, based on the Kullback-Leibler and quadratic Rényi relative entropies. Additionally, we propose estimators of their asymptotic variances. Even though the objective of EB is to reduce model dependence, the estimators are generally not consistent unless implicit parametric assumptions for the propensity score or conditional outcomes are met. The finite sample properties of the estimators are investigated through a simulation study. In an application with observational data from the Swedish Childhood Diabetes Register, we estimate the average effect of school achievements on hospitalization due to acute complications of type 1 diabetes mellitus.
