Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting.
Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting.
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DOI：
10.1111/rssb.12129
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发表时间：
2016-06
期刊：
影响因子：
--
通讯作者：
Zhang Z
中科院分区：
文献类型：
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作者：
Chan KC;Yam SC;Zhang Z
根据观测数据估计平均治疗效果在实践中极其重要，几代统计学家在不同的框架下对此进行了研究。现有的全局有效估计量需要对倾向得分函数、结果回归函数或两者都进行非参数估计，但在实际样本量下，它们的性能可能较差。在没有明确估计任何一个函数的情况下，我们考虑了一大类校正权重，其构造是为了在处理组、对照组和组合组之间实现观测协变量的矩的精确三向平衡。广泛的分类包括指数倾斜、经验似然和广义回归作为重要的特例，并将调查校准估计扩展到不同的统计问题和具有重要区别的情况。对于这类一般的校准估计量，建立了估计平均治疗效应的全局半参数效率。结果表明，仅通过平衡协变量分布就可以达到效率，而不需要直接估计倾向分数或结果回归函数。我们还提出了有效渐近方差的一致估计，它不涉及对倾向得分或结果回归函数的额外函数估计。所提出的方差估计器的性能优于现有的需要有效影响函数的直接近似的估计器。
The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either functions, we consider a wide class calibration weights constructed to attain an exact three-way balance of the moments of observed covariates among the treated, the control, and the combined group. The wide class includes exponential tilting, empirical likelihood and generalized regression as important special cases, and extends survey calibration estimators to different statistical problems and with important distinctions. Global semiparametric efficiency for the estimation of average treatment effects is established for this general class of calibration estimators. The results show that efficiency can be achieved by solely balancing the covariate distributions without resorting to direct estimation of propensity score or outcome regression function. We also propose a consistent estimator for the efficient asymptotic variance, which does not involve additional functional estimation of either the propensity score or the outcome regression functions. The proposed variance estimator outperforms existing estimators that require a direct approximation of the efficient influence function.