摘要翻译：
在随机对照试验中，可以通过收集额外个体的数据或收集预测结果变量的额外协变量来提高平均治疗效果估计器的精度。我们建议使用预实验数据，如人口普查，或家庭调查，以通知选择样本量和协变量收集。我们的程序寻求最小化结果的平均治疗效果估计者的均方误差，受研究人员的预算限制。我们依赖于一个正交贪婪算法的修改，该算法在存在大量潜在协变量的情况下概念简单，易于实现，并且不需要任何调整参数。在两个经验应用中，我们表明我们的程序可以导致高达58%的实质性收益，无论是从数据收集成本的减少还是从治疗效果估计器精度的提高来衡量。
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英文标题：
《Optimal Data Collection for Randomized Control Trials》
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作者：
Pedro Carneiro, Sokbae Lee, Daniel Wilhelm
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最新提交年份：
2016
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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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英文摘要：
  In a randomized control trial, the precision of an average treatment effect estimator can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. We propose the use of pre-experimental data such as a census, or a household survey, to inform the choice of both the sample size and the covariates to be collected. Our procedure seeks to minimize the resulting average treatment effect estimator's mean squared error, subject to the researcher's budget constraint. We rely on a modification of an orthogonal greedy algorithm that is conceptually simple and easy to implement in the presence of a large number of potential covariates, and does not require any tuning parameters. In two empirical applications, we show that our procedure can lead to substantial gains of up to 58%, measured either in terms of reductions in data collection costs or in terms of improvements in the precision of the treatment effect estimator.
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PDF链接：
https://arxiv.org/pdf/1603.03675