不要让你的分析进入种子:关于随机种子对基于机器学习的因果推理的影响
Lindsey Schader1, Weishan Song1, Russell Kempker2
1From the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA.
Epidemiology (Cambridge, Mass.)
|August 16, 2024
概括
随机种子显著影响机器学习因果效应估计,特别是双倍可靠的估计器. 我们介绍了稳定技术,以确保流行病学分析的可靠结果.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 计算机科学 计算机科学
背景情况:
- 机器学习 (ML) 提高了流行病学中的因果效应估计,减少了对严格模型假设的依赖.
- 机器学习算法的固有随机性可以导致结果受到初始随机种子的影响.
- 双重可靠的估计器是用于因果推理的流行的ML方法.
研究的目的:
- 调查随机种子对因果效应估计中的双倍强大的估计器的影响.
- 开发和验证稳定基于ML的因果估计与随机种子变异性的方法.
- 为在流行病学研究中尽量减少与种子相关的偏见提供实际指导.
主要方法:
- 检查了不同随机种子对使用单一数据集的双倍强大的估计器的影响.
- 开发和实施技术,以稳定不同随机种子的估计结果.
- 进行了广泛的模拟研究,以评估稳定方法的有效性.
主要成果:
- 证明不同的随机种子可以导致对因果估计的科学解释分歧.
- 展示了拟议的稳定技术有效地减轻了与种子相关的变化.
- 确认稳定不影响估计器的统计效率.
结论:
- 随机种子在基于ML的因果效应估计中引入了实质性的变异性,影响了科学结论.
- 稳定技术提供了一个强大的解决方案,以确保流行病学研究中的可重复和可靠的结果.
- 研究人员应积极解决并报告ML应用中的随机种子影响,以获得因果推理.
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