戳穿虚假：带噪声对照数据的自适应调整的贝叶斯模型
Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data
AI总结：
本文提出一种基于贝叶斯层次建模的自适应调整方法，在重复实验中利用数据决定调整程度，相比差异估计提高统计效率，并在真实和模拟数据上验证其实际影响。
摘要：
在因果推断中，如何调整对照数据以实现偏差和方差之间的平衡并不总是明确的。我们展示了在重复实验的背景下，贝叶斯层次建模如何产生一种自适应程序，该程序利用数据来确定应进行多少调整。与基于差异估计的默认分析相比，这一结果提供了一种新颖的分析方法，具有更高的统计效率。我们在两个真实示例以及一系列模拟数据集上演示了这一程序。我们表明，效率的提高在从实验中可以得出的结论方面可能具有现实世界的影响。我们还讨论了这项工作对因果推断以及更广泛的统计设计和分析的相关性。
英文摘要：
It is not always clear how to adjust for control data in causal inference, balancing the goals of reducing bias and variance. We show how, in a setting with repeated experiments, Bayesian hierarchical modeling yields an adaptive procedure that uses the data to determine how much adjustment to perform. The result is a novel analysis with increased statistical efficiency compared to the default analysis based on difference estimates. We demonstrate this procedure on two real examples, as well as on a series of simulated datasets. We show that the increased efficiency can have real-world consequences in terms of the conclusions that can be drawn from the experiments. We also discuss the relevance of this work to causal inference and statistical design and analysis more generally.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
