论文标题
倾向得分结构在估计条件分位治疗效应的渐近效率中的作用
The Role of Propensity Score Structure in Asymptotic Efficiency of Estimated Conditional Quantile Treatment Effect
论文作者
论文摘要
当给出严格的协变量子集时，我们提出有条件的分分处理效果，以通过分位数片段捕获治疗效应的异质性，这是给定协变量和分位数的功能。我们专注于在参数，非参数和半摩托结构下得出基于概率得分的估计量的渐近正态性。我们对估计效率进行了系统的研究，以检查倾向得分结构的重要性以及与无条件对应物的基本差异。派生的独特属性可以回答：这些估计器的一般排名是多少？给定协变量的隶属关系与倾向得分的协变量集如何影响效率？估计倾向评分的收敛速率如何影响效率？为什么在实践中值得推荐半参数估计？我们还简要讨论了处理大小方案以及渐近方差估计的方法的扩展。进行了模拟研究以检查这些估计值的性能。分析了一个真实的数据示例以进行插图，并获得了一些新发现。
When a strict subset of covariates are given, we propose conditional quantile treatment effect to capture the heterogeneity of treatment effects via the quantile sheet that is the function of the given covariates and quantile. We focus on deriving the asymptotic normality of probability score-based estimators under parametric, nonparametric and semiparametric structure. We make a systematic study on the estimation efficiency to check the importance of propensity score structure and the essential differences from the unconditional counterparts. The derived unique properties can answer: what is the general ranking of these estimators? how does the affiliation of the given covariates to the set of covariates of the propensity score affect the efficiency? how does the convergence rate of the estimated propensity score affect the efficiency? and why would semiparametric estimation be worth of recommendation in practice? We also give a brief discussion on the extension of the methods to handle large-dimensional scenarios and on the estimation for the asymptotic variances. The simulation studies are conducted to examine the performances of these estimators. A real data example is analyzed for illustration and some new findings are acquired.
