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摘要翻译：
提出了一种在不混淆和存在高维或非参数干扰参数的情况下，连续治疗变量因果效应的非参数推断方法。我们的平均剂量-反应函数（或平均结构函数）和部分效应的双去偏机器学习(DML)估计是渐近正态的，具有非参数收敛速度。条件期望函数和条件密度的干扰估计可以是非参数或ML方法。利用基于核的双鲁棒矩函数和交叉拟合，给出了干扰估计不影响DML估计的一阶大样本分布的高级条件。我们进一步为核和级数估计量，以及现代ML方法-广义随机森林和深度神经网络提供了充分的低级条件。我们通过Gateaux导数证明了使用核将连续处理局部化在给定值的合理性。我们在蒙特卡罗模拟中实现了各种ML方法，并在一个工作培训方案评估中进行了实证应用。
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英文标题：
《Double Debiased Machine Learning Nonparametric Inference with Continuous
Treatments》
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作者：
Kyle Colangelo and Ying-Ying Lee
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最新提交年份：
2021
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
We propose a nonparametric inference method for causal effects of continuous treatment variables, under unconfoundedness and in the presence of high-dimensional or nonparametric nuisance parameters. 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 nonparametric convergence rates. The nuisance estimators for the 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 estimators do not affect the first-order large sample distribution of the DML estimators. We further provide sufficient low-level conditions for kernel and series estimators, as well as modern ML methods - generalized random forests 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.
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PDF链接：
https://arxiv.org/pdf/2004.03036
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