摘要翻译：
实证研究人员越来越多地面对包含许多控制或工具变量的丰富数据集，这使得选择合适的变量选择方法变得至关重要。在本文中，我们给出了用后正交或正交的$L_2$-Boosting进行变量选择后的有效推断的结果。我们在许多控制变量和可能有许多仪器的工具变量模型中选择后考虑治疗效果。为了达到这一目的，我们在类似于Lasso的高维环境下，即在近似稀疏性条件下，在不假定beta-min条件下，建立了迭代后$L2$Boosting和正交$L2$Boosting的收敛速度的新结果。将这些结果推广到2SLS框架中，为治疗效果分析提供了有效的推断。我们对所提出的方法给出了大量的仿真结果，并与LASSO进行了比较。在实证应用中，我们用我们提出的方法构造了有效的IVs来估计并购前美国银行分支网络重叠对并购后银行股票收益的影响。
---
英文标题：
《Estimation and Inference of Treatment Effects with $L_2$-Boosting in
  High-Dimensional Settings》
---
作者：
Jannis Kueck, Ye Luo, Martin Spindler, Zigan Wang
---
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：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
--

---
英文摘要：
  Empirical researchers are increasingly faced with rich data sets containing many controls or instrumental variables, making it essential to choose an appropriate approach to variable selection. In this paper, we provide results for valid inference after post- or orthogonal $L_2$-Boosting is used for variable selection. We consider treatment effects after selecting among many control variables and instrumental variable models with potentially many instruments. To achieve this, we establish new results for the rate of convergence of iterated post-$L_2$-Boosting and orthogonal $L_2$-Boosting in a high-dimensional setting similar to Lasso, i.e., under approximate sparsity without assuming the beta-min condition. These results are extended to the 2SLS framework and valid inference is provided for treatment effect analysis. We give extensive simulation results for the proposed methods and compare them with Lasso. In an empirical application, we construct efficient IVs with our proposed methods to estimate the effect of pre-merger overlap of bank branch networks in the US on the post-merger stock returns of the acquirer bank.
---
PDF链接：
https://arxiv.org/pdf/1801.00364