摘要翻译：
本文研究了模型选择后平均治疗效果的鲁棒性推断。在可观测选择框架中，我们给出了如何构造基于对模型选择误差具有鲁棒性的双鲁棒估计的置信区间，并证明了它们在一大类治疗效果模型上是一致有效的。这个类允许具有异质性效应的多值处理（在可观察到的情况下），一般的异方差性，以及在（可能）比观察到的更多的协变量中进行选择。在适当的条件下，我们的估计得到了半参数有效界。给出了任何模型选择器产生这些结果的精确条件，并展示了如何将数据驱动的选择与经济理论结合起来。在具体实现上，我们给出了一个基于组lasso的选择方案，它特别适合于治疗效果数据，并对高维稀疏多项式logistic回归导出了新的结果。仿真研究表明，我们的估计器在有限样本下，在广泛的模型范围内都有很好的性能。重新审视国家支持的工作示范数据，我们的方法产生了准确的估计和严格的置信区间。
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英文标题：
《Robust Inference on Average Treatment Effects with Possibly More
  Covariates than Observations》
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作者：
Max H. Farrell
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最新提交年份：
2018
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分类信息：

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
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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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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
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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。
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英文摘要：
  This paper concerns robust inference on average treatment effects following model selection. In the selection on observables framework, we show how to construct confidence intervals based on a doubly-robust estimator that are robust to model selection errors and prove that they are valid uniformly over a large class of treatment effect models. The class allows for multivalued treatments with heterogeneous effects (in observables), general heteroskedasticity, and selection amongst (possibly) more covariates than observations. Our estimator attains the semiparametric efficiency bound under appropriate conditions. Precise conditions are given for any model selector to yield these results, and we show how to combine data-driven selection with economic theory. For implementation, we give a specific proposal for selection based on the group lasso, which is particularly well-suited to treatment effects data, and derive new results for high-dimensional, sparse multinomial logistic regression. A simulation study shows our estimator performs very well in finite samples over a wide range of models. Revisiting the National Supported Work demonstration data, our method yields accurate estimates and tight confidence intervals.
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PDF链接：
https://arxiv.org/pdf/1309.4686