Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting

• 成果类型: Article
• 署名作者: Chan, Kwun Chuen Gary; Yam, Sheung Chi Phillip; Zhang, Zheng
• 署名单位: University of Washington; University of Washington Seattle; Chinese University of Hong Kong
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12129
• 发表日期: 2016
• 页码: 673-700
• 关键词: demystifying double robustness; missing response problem; moment condition models; propensity score; incomplete data; alternative strategies; training-programs; sample properties; Causal Inference; auxiliary data

摘要

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 function, we consider a wide class of 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 the 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 variance estimator proposed outperforms existing estimators that require a direct approximation of the efficient influence function.