Application of the Generalized Empirical Likelihood Method Based on Inverse Probability Weighting in the Estimation of Average Treatment Effects

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。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.