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摘要翻译：
在许多情况下，研究人员对估计平均治疗效果感兴趣，并愿意依赖于不混杂性假设，这要求治疗分配与治疗前变量的随机条件一样好。如果在分析中包括大量预处理变量，则不混杂性假设往往更合理，但这可能会恶化处理效果估计的标准方法的性能。在本文中，我们提出了一种去偏置惩罚回归调整的方法，允许像lasso这样的稀疏回归方法用于高维线性模型中平均治疗效果的sqrt{n}一致推断。给定线性，我们不需要假设治疗倾向是可估计的，或者平均治疗效果是结果模型参数的稀疏对比。相反，除了用于使结果模型上的lasso回归在1-范数误差下一致的标准假设之外，我们只要求重叠，即倾向得分在远离0和1的地方一致有界。在程序上，我们的方法将平衡权重与正则化回归调整相结合。
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英文标题：
《Approximate Residual Balancing: De-Biased Inference of Average Treatment
Effects in High Dimensions》
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作者：
Susan Athey, Guido W. Imbens, and Stefan Wager
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最新提交年份：
2018
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分类信息：
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：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：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：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：
There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment assignment be as good as random conditional on pre-treatment variables. The unconfoundedness assumption is often more plausible if a large number of pre-treatment variables are included in the analysis, but this can worsen the performance of standard approaches to treatment effect estimation. In this paper, we develop a method for de-biasing penalized regression adjustments to allow sparse regression methods like the lasso to be used for sqrt{n}-consistent inference of average treatment effects in high-dimensional linear models. Given linearity, we do not need to assume that the treatment propensities are estimable, or that the average treatment effect is a sparse contrast of the outcome model parameters. Rather, in addition standard assumptions used to make lasso regression on the outcome model consistent under 1-norm error, we only require overlap, i.e., that the propensity score be uniformly bounded away from 0 and 1. Procedurally, our method combines balancing weights with a regularized regression adjustment.
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PDF链接：
https://arxiv.org/pdf/1604.07125
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