通过观察研究设计实验
Designing Experiments Informed by Observational Studies
摘要：
随着被动观测数据的不断增加，人们对“数据融合”方法的兴趣也越来越大。“这些方法涉及合并来自观察和实验来源的数据以得出因果结论-它们通常需要在观察数据集中的未知偏差和实验数据集中通常较大的方差之间进行不稳定的权衡。我们提出了一种利用观测数据的替代方法，它避免了这种权衡：而不是使用观测数据进行推理，我们用它来设计一个更有效的实验。我们考虑的情况下，一个分层的实验与一个二元的结果，并假设试点估计层潜在的结果方差可以从观察性研究。我们扩展了Zhao et al.（2019）的结果，以便为这些方差生成置信集，同时考虑到不可测量的混杂的可能性。然后，我们提出的试验设计问题之一的遗憾最小化，受到我们的信心集所施加的约束。我们表明，这个问题可以转换成一个凸最小化，并使用传统的方法解决。最后，我们证明了我们的方法使用的数据从妇女的健康倡议的实际效用。
英文摘要：
The increasing availability of passively observed data has yielded a growing methodological interest in "data fusion." These methods involve merging data from observational and experimental sources to draw causal conclusions -- and they typically require a precarious tradeoff between the unknown bias in the observational dataset and the often-large variance in the experimental dataset. We propose an alternative approach to leveraging observational data, which avoids this tradeoff: rather than using observational data for inference, we use it to design a more efficient experiment. We consider the case of a stratified experiment with a binary outcome, and suppose pilot estimates for the stratum potential outcome variances can be obtained from the observational study. We extend results from Zhao et al. (2019) in order to generate confidence sets for these variances, while accounting for the possibility of unmeasured confounding. Then, we pose the experimental design problem as one of regret minimization, subject to the constraints imposed by our confidence sets. We show that this problem can be converted into a convex minimization and solved using conventional methods. Lastly, we demonstrate the practical utility of our methods using data from the Women's Health Initiative.
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