不要让你的分析变成种子:随机种子对基于机器学习的因果推理的影响。

IF 4.7 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Epidemiology Pub Date : 2024-11-01 Epub Date: 2024-08-16 DOI:10.1097/EDE.0000000000001782
Lindsey Schader, Weishan Song, Russell Kempker, David Benkeser
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引用次数: 0

摘要

用于因果效应估计的机器学习技术可以提高流行病学分析的可靠性,减少对正确模型规格的依赖。然而,许多机器学习算法的随机性意味着,这些方法得出的结果可能会受到模型拟合前设置的随机种子的影响。在这项工作中,我们强调了随机种子对基于机器学习的因果效应估计的一种流行方法(即双重稳健估计器)的重大影响。我们说明,不同的种子会对同一数据集产生的双重稳健估计产生不同的科学解释。我们提出了稳定随机种子结果的技术,并通过广泛的模拟研究证明,这些技术能有效中和与种子相关的变异性,而不会影响估计器的统计效率。基于这些发现,我们提出了在实际应用中尽量减少随机种子影响的实用指南,并鼓励研究人员在实施任何涉及随机步骤的方法时,探索随机种子导致的变异性。
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Don't Let Your Analysis Go to Seed: On the Impact of Random Seed on Machine Learning-based Causal Inference.

Machine learning techniques for causal effect estimation can enhance the reliability of epidemiologic analyses, reducing their dependence on correct model specifications. However, the stochastic nature of many machine learning algorithms implies that the results derived from such approaches may be influenced by the random seed that is set before model fitting. In this work, we highlight the substantial influence of random seeds on a popular approach for machine learning-based causal effect estimation, namely doubly robust estimators. We illustrate that varying seeds can yield divergent scientific interpretations of doubly robust estimates produced from the same dataset. We propose techniques for stabilizing results across random seeds and, through an extensive simulation study, demonstrate that these techniques effectively neutralize seed-related variability without compromising the statistical efficiency of the estimators. Based on these findings, we offer practical guidelines to minimize the influence of random seeds in real-world applications, and we encourage researchers to explore the variability due to random seeds when implementing any method that involves random steps.

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来源期刊
Epidemiology
Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
6.70
自引率
3.70%
发文量
177
审稿时长
6-12 weeks
期刊介绍: Epidemiology publishes original research from all fields of epidemiology. The journal also welcomes review articles and meta-analyses, novel hypotheses, descriptions and applications of new methods, and discussions of research theory or public health policy. We give special consideration to papers from developing countries.
期刊最新文献
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