摘要翻译:
本文在数据丰富的环境中,给出了包括局部平均(LATE)和局部分位数处理效应(LQTE)在内的各种处理效应的有效估计量和诚实置信带。我们可以处理非常多的控制变量,内源性的治疗接受,异质性的治疗效果,和功能价值的结果。我们的框架涵盖了外源性接受治疗的特殊情况,无论是有条件的对照还是无条件的随机对照试验。在后一种情况下,我们的方法为(函数)平均处理效应(ATE)和分位数处理效应(QTE)产生有效的估计量和诚实的带。为了使信息推理成为可能,我们假设键约简形式的预测关系近似稀疏。这个假设允许使用正则化和选择方法来估计这些关系,并且我们提供了在广泛的模型范围内一致有效(诚实)的后正则化和后选择推理的方法。我们证明,在估计某些约简形式的函数参数时,使用正交或双鲁棒矩条件是实现诚实推理的一个关键因素。我们用一个应用来说明所提出的方法在估计401(k)资格和参与对累积资产的影响方面的应用。
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英文标题:
《Program Evaluation and Causal Inference with High-Dimensional Data》
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作者:
Alexandre Belloni and Victor Chernozhukov and Ivan Fern\'andez-Val and
Christian Hansen
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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: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
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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:
In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function-valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post-regularization and post-selection inference that are uniformly valid (honest) across a wide-range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets.
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PDF链接:
https://arxiv.org/pdf/1311.2645