基于逆概率加权的广义经验似然方法在平均处理效应估计中的应用
Application of the Generalized Empirical Likelihood Method Based on Inverse Probability Weighting in the Estimation of Average Treatment Effects
摘要: 传统的经验似然方法在处理复杂模型时存在计算效率低和灵活性不足的局限，广义经验似然方法通过引入更一般的权重函数和优化目标，提高了模型的适应性和计算效率。本文分析了名为NSW的数据集，其中包含职业培训对收入影响的相关数据。基于线性模型和logit模型的得分函数，构造矩条件，通过广义经验似然方法得到了平均处理效应的估计结果。对比于单一的逆概率加权方法，基于逆概率加权的广义经验似然方法的估计结果较为稳健。
Abstract: The traditional empirical likelihood method has limitations in terms of computational efficiency and flexibility when dealing with complex models. The generalized empirical likelihood method addresses these issues by introducing more general weight functions and optimization objectives, thereby enhancing the adaptability and computational efficiency of the model. This paper analyzes the NSW dataset, which examines the impact of vocational training on income. Based on the score functions of the linear model and the logit model, moment conditions are constructed, and the average treatment effect is estimated using the generalized empirical likelihood method. Compared to the single inverse probability weighting method, the estimation results of the generalized empirical likelihood method based on inverse probability weighting are more robust.
文章引用：宁高佳, 侯文. 基于逆概率加权的广义经验似然方法在平均处理效应估计中的应用[J]. 应用数学进展, 2025, 14(5): 72-81. https://doi.org/10.12677/aam.2025.145234

参考文献

[1] Owen, A.B. (1988) Empirical Likelihood Ratio Confidence Intervals for a Single Functional. Biometrika, 75, 237-249. [Google Scholar] [CrossRef] 
[2] Qin, J. and Lawless, J. (1994) Empirical Likelihood and General Estimating Equations. The Annals of Statistics, 22, 300-325. [Google Scholar] [CrossRef] 
[3] Wooldridge, J.M. (2002) Inverse Probability Weighted M-Estimators for Sample Selection, Attrition, and Stratification. Portuguese Economic Journal, 1, 117-139. [Google Scholar] [CrossRef] 
[4] Rubin, D.B. (1974) Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology, 66, 688-701. [Google Scholar] [CrossRef] 
[5] Zhao, Q. and Percival, D. (2016) Entropy Balancing Is Doubly Robust. Journal of Causal Inference, 5, 1-19. [Google Scholar] [CrossRef] 
[6] Zhang, J., Shi, H., Tian, L. and Xiao, F. (2019) Penalized Generalized Empirical Likelihood in High-Dimensional Weakly Dependent Data. Journal of Multivariate Analysis, 171, 270-283. [Google Scholar] [CrossRef] 
[7] Hill, J.B. (2015) Robust Generalized Empirical Likelihood for Heavy Tailed Autoregressions with Conditionally Heteroscedastic Errors. Journal of Multivariate Analysis, 135, 131-152. [Google Scholar] [CrossRef] 
[8] Ogata, H. (2013) Estimation for Multivariate Stable Distributions with Generalized Empirical Likelihood. Journal of Econometrics, 172, 248-254. [Google Scholar] [CrossRef] 
[9] 牛翔宇, 冯予. 缺失数据下广义非线性回归的经验似然及诊断[J]. 重庆工商大学学报, 2016, 33(6): 15-21.
[10] Guggenberger, P. and Smith, R.J. (2005) Generalized Empirical Likelihood Estimators and Tests Under Partial, Weak, and Strong Identification. Econometric Theory, 21, 667-709.
[11] Jin, F. and Lee, L. (2019) GEL Estimation and Tests of Spatial Autoregressive Models. Journal of Econometrics, 208, 585-612. [Google Scholar] [CrossRef] 
[12] Li, Y. and Qin, Y. (2022) Empirical Likelihood for Spatial Dynamic Panel Data Models. Journal of the Korean Statistical Society, 51, 500-525. [Google Scholar] [CrossRef] [PubMed]
[13] Corcoran, S. (1998) Bartlett Adjustment of Empirical Discrepancy Statistics. Biometrika, 85, 967-972. [Google Scholar] [CrossRef] 
[14] Rosenbaum, P.R. and Rubin, D.B. (1983) The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70, 41-55. [Google Scholar] [CrossRef] 

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