超越重权:关于共变量转移在效应概括中的预测作用
Ying Jin1, Naoki Egami2, Dominik Rothenhäusler3
1Data Science Initiative & Department of Health Care Policy, Harvard University, Cambridge, MA 02138.
概括
仅仅对共变量轮班调整不足以进行概括. 然而,这项研究表明,共变量转移可以预测未知的条件转移,改善统计推理和不确定性量化在整个人口.
科学领域:
- 统计推断的统计推断.
- 分布转移分析分布式转移分析
- 概括性研究的研究.
背景情况:
- 现有的概括方法通常依赖于共变量转移假设.
- 仅仅通过对变量调节的调整,就不足以解决分配变化的问题.
- 未观察到的变量的条件转移显著影响可概括的推理.
研究的目的:
- 调查共变量转移和条件转移之间的预测关系.
- 为量化分配转移提出标准化措施.
- 在一般化任务中证明改进的不确定性量化.
主要方法:
- 分析了两项大型多站点复制研究 (680项研究,65个站点).
- 开发和应用标准化,关键措施的共同变量和条件转移.
- 使用随机分布转移模型进行理论解释.
主要成果:
- 共变量转移可以预测未观察到的条件转移的强度.
- 条件转移,虽然不可忽视,但往往被可观测的共同变量转移所限制.
- 拟议的措施使可靠和高效的不确定性定量化为一般化.
结论:
- 共变量转移为条件转移提供了有价值的预测见解.
- 标准化转移措施对于理解分布式转移至关重要.
- 这种方法增强了外部有效性和可靠的统计推理.
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