为估计因果效应的算法选择:使用无卵性妊娠结果研究的一个例子:监测将要成为母亲的母亲
Zhaohua Zeng1, Lisa M Bodnar2, Ashley I Naimi1
1Department of Epidemiology, Emory University.
Epidemiology (Cambridge, Mass.)
|August 15, 2025
概括
建议使用多样化的超级学习者组合,具有两倍强大的估计器. 在因果推理研究中包含许多算法,比如估计饮食对孕前风险的影响,可以提高估计稳定性.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 超级学习者是用于因果效应估计的整体方法.
- 它通常与两倍强大的估计器一起使用.
- 建议在算法库中的多样性,但其影响尚未得到充分量化.
研究的目的:
- 评估算法库大小对超级学习者表现的影响.
- 评估因果效应估计对图书馆组成的敏感性.
- 为了调查围绕怀孕的饮食和孕前风险之间的关联.
主要方法:
- 应用超级学习与增强的逆概率权重 (AIPW) 和针对性的基于最小损失的估计 (TMLE).
- 估计了水果和蔬菜摄入量对7923名妇女产前风险的平均治疗效应 (ATE).
- 从参考集合与算法的子集和单个算法的比较估计.
主要成果:
- 高水果和蔬菜摄入量 (≥2.5杯/1000卡) 与较低的孕前风险有关.
- 对AIPW和TMLE的ATE估计分别为-0.019和-0.023.
- 删除单个算法的影响最小,但使用单个算法增加了可变性.
结论:
- 经验证据支持在超级学习器组合中使用各种机器学习算法.
- 这种方法提高了因果效应估计的可靠性,从两倍可靠的估计器.
- 多样化的集合对于流行病学研究中可靠的发现至关重要.
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