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Optimal data collection for randomized control trials

机译:随机控制试验的最佳数据收集

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摘要

In a randomized control trial, the precision of an average treatment effect estimator and the power of the corresponding t-test can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. To design the experiment, a researcher needs to solve this trade-off subject to her budget constraint. We show that this optimization problem is equivalent to optimally predicting outcomes by the covariates, which in turn can be solved using existing machine learning techniques using pre-experimental data such as other similar studies, a census, or a household survey. In two empirical applications, we show that our procedure can lead to reductions of up to 58% in the costs of data collection, or improvements of the same magnitude in the precision of the treatment effect estimator.
机译:在随机控制试验中,通过在额外个体上收集数据,或通过收集预测结果变量的额外协变量,可以改善平均处理效果估计器的精度和相应的T检验的功率。为了设计实验,研究人员需要解决这一权衡受预算限制的影响。我们表明,这种优化问题相当于协变量的最佳预测结果,这反过来可以使用现有的机器学习技术使用诸如其他类似研究,人口普查或家庭调查的预先实验数据来解决。在两个经验应用中,我们表明,我们的程序可以导致数据收集成本降低高达58%,或者在治疗效果估算器的精度中的提高程度的提高。

著录项

  • 来源
    《The econometrics journal》 |2020年第1期|1-31|共31页
  • 作者单位

    University College London Gower Street London WC1E 6BT UK. Institute for Fiscal Studies 7 Ridgmount Street London WC1E 7AE UK. Centre for Microdata Methods and Practice 7 Ridgmount Street London WC1E 7AE U.K.;

    Columbia University 420 West 118th Street New York NY 10027 USA.University College London Gower Street London WC1E 6BT UK.Institute for Fiscal Studies 7 Ridgmount Street London WC1E 7AE UK.;

    University College London Gower Street London WC1E 6BT UK. Institute for Fiscal Studies 7 Ridgmount Street London WC1E 7AE UK. Centre for Microdata Methods and Practice 7 Ridgmount Street London WC1E 7AE U.K.;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    data collection; randomized control trials; machine learning;

    机译:数据采集​​;随机控制试验;机器学习;

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