论文标题
双重机器学习非参数推断，并连续治疗
Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments
论文作者
论文摘要
我们提出了一种双重鲁棒的推理方法，用于在不相处，非参数或高维滋扰功能下连续治疗变量的因果关系。我们对平均剂量反应函数（或平均结构函数）和部分效应的双重机器学习（DML）估计器在非参数收敛速率上是渐近正常的。滋扰条件期望函数和条件密度的第一步估计器可以是非参数或ML方法。利用基于内核的双重稳健力矩函数和交叉拟合，我们提供了高水平的条件，在该条件下，令人讨厌的函数估计器不会影响DML估计器的一阶大样本分布。我们为内核，系列和深层神经网络提供足够的低水平条件。我们证明使用内核在Gateaux衍生物以给定值的连续处理定位。我们在蒙特卡洛模拟中实施了各种ML方法，并在职业培训计划评估中实施了经验应用
We propose a doubly robust inference method for causal effects of continuous treatment variables, under unconfoundedness and with nonparametric or high-dimensional nuisance functions. Our double debiased machine learning (DML) estimators for the average dose-response function (or the average structural function) and the partial effects are asymptotically normal with non-parametric convergence rates. The first-step estimators for the nuisance conditional expectation function and the conditional density can be nonparametric or ML methods. Utilizing a kernel-based doubly robust moment function and cross-fitting, we give high-level conditions under which the nuisance function estimators do not affect the first-order large sample distribution of the DML estimators. We provide sufficient low-level conditions for kernel, series, and deep neural networks. We justify the use of kernel to localize the continuous treatment at a given value by the Gateaux derivative. We implement various ML methods in Monte Carlo simulations and an empirical application on a job training program evaluation
