摘要翻译:
本文给出了条件期望函数的正则(半参数)和非正则(非参数)线性函数的基于$\ell_1$正则化的自适应推理方法。常规函数的例子包括平均待遇效应、政策效应和导数。非正则函数的例子包括平均治疗效应、政策效应和以固定在一点上的协变量子向量为条件的导数。我们构造了一个目标参数的Neyman正交方程,该方程对干扰参数的小扰动近似不变。为了实现此属性,我们将函数的Riesz表示器作为一个额外的讨厌参数。我们的分析得到了弱的“双稀疏鲁棒性”:要么对回归的逼近,要么对表征的逼近,只要对方足够“稀疏”,都可以是“完全稠密的”。我们的主要结果是非渐近的,并暗示了在大类模型上的渐近一致有效性,转化为全局和局部参数的诚实置信带。
---
英文标题:
《De-Biased Machine Learning of Global and Local Parameters Using
Regularized Riesz Representers》
---
作者:
Victor Chernozhukov, Whitney Newey, Rahul Singh
---
最新提交年份:
2021
---
分类信息:
一级分类:Statistics 统计学
二级分类:Machine Learning 机器学习
分类描述:Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文(监督,无监督,半监督学习,图形模型,强化学习,强盗,高维推理等)与统计或理论基础
--
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。
--
一级分类:Mathematics 数学
二级分类:Statistics Theory 统计理论
分类描述:Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计:例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
--
一级分类:Statistics 统计学
二级分类:Statistics Theory 统计理论
分类描述:stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近,贝叶斯推论,决策理论,估计,基础,推论,检验。
--
---
英文摘要:
We provide adaptive inference methods, based on $\ell_1$ regularization, for regular (semiparametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects, and derivatives. Examples of non-regular functionals include average treatment effects, policy effects, and derivatives conditional on a covariate subvector fixed at a point. We construct a Neyman orthogonal equation for the target parameter that is approximately invariant to small perturbations of the nuisance parameters. To achieve this property, we include the Riesz representer for the functional as an additional nuisance parameter. Our analysis yields weak "double sparsity robustness": either the approximation to the regression or the approximation to the representer can be "completely dense" as long as the other is sufficiently "sparse". Our main results are non-asymptotic and imply asymptotic uniform validity over large classes of models, translating into honest confidence bands for both global and local parameters.
---
PDF链接:
https://arxiv.org/pdf/1802.08667