构建因果生命周期模型:数据驱动和理论驱动方法的比较研究.
American journal of epidemiology
|June 21, 2023
概括
数据驱动的因果发现算法可以通过识别可信的因果关系来帮助生命周期流行病学,补充专家驱动的模型. 结合这两种方法可以加强因果模型的构建.
科学领域:
- 生命过程流行病学
- 因果推理的原因推理.
- 纵向数据分析的数据分析.
背景情况:
- 生命周期流行病学需要复杂的因果模型来理解随时间变化的相互作用.
- 传统上,这些模型是从现有的理论和先前的研究中构建的.
研究的目的:
- 调查数据驱动的因果发现算法是否可以帮助构建生命周期流行病学因果模型.
- 将数据驱动算法生成的模型与主题领域专家开发的模型进行比较.
主要方法:
- 使用了纵向大都会研究数据集 (丹麦男性,1953-2017年).
- 建立基于专家知识的理论驱动模型.
- 使用时间彼得 - 克拉克 (TPC) 算法生成数据驱动模型,利用时间信息.
主要成果:
- 从专家模型中,TPC算法确定了一些,但不是所有的因果关系.
- 数据驱动的方法在检测直接因果关系方面表现出色,专家信心很高.
- 数据驱动模型提出的大量新的因果关系在审查后被认为是合理的.
结论:
- 数据驱动的方法可以有效地支持生命周期流行病学中的因果模型开发.
- 时间彼得 - 克拉克算法提供了有价值的见解,可能揭示了被忽视的因果假设.
- 整合数据驱动和理论驱动的方法可以产生更强大和更全面的因果模型.
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