标题： 非可分模型与高维数据 显示英文标题

标题： Non-separable Models with High-dimensional Data

摘要： 本文研究当控制变量的维度较高且可能大于有效样本量时，具有连续处理的不可分离模型。 我们提出了一种三步估计过程来估计平均、分位数和边际处理效应。 在第一步中，我们分别通过惩罚局部最小二乘法、惩罚局部最大似然估计和惩罚条件密度估计来估计条件均值、分布和密度对象，在每个连续处理值处通过局部化的$L_{1}$-惩罚方法选择控制变量。 在第二步中，我们通过插件原理估计潜在结果的平均值和边际分布。 在第三步中，我们分别通过反转估计的分布函数和使用局部线性回归来估计分位数和边际处理效应。 我们研究了这些估计量的渐近性质，并提出了一个加权Bootstrap方法进行推断。 使用模拟和真实数据集，我们展示了所提出的估计量在有限样本中表现良好。 显示英文摘要

摘要： This paper studies non-separable models with a continuous treatment when the dimension of the control variables is high and potentially larger than the effective sample size. We propose a three-step estimation procedure to estimate the average, quantile, and marginal treatment effects. In the first stage we estimate the conditional mean, distribution, and density objects by penalized local least squares, penalized local maximum likelihood estimation, and penalized conditional density estimation, respectively, where control variables are selected via a localized method of $L_{1}$ -penalization at each value of the continuous treatment. In the second stage we estimate the average and the marginal distribution of the potential outcome via the plug-in principle. In the third stage, we estimate the quantile and marginal treatment effects by inverting the estimated distribution function and using the local linear regression, respectively. We study the asymptotic properties of these estimators and propose a weighted-bootstrap method for inference. Using simulated and real datasets, we demonstrate the proposed estimators perform well in finite samples.