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考虑使用基于树的机器学习来评估人口和环境风险因素与健康结果之间的因果关系

Daniela Galatro1, Alessia Di Nardo2, Varun Pai3

  • 1Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada. daniela.galatro@utoronto.ca.

Environmental science and pollution research international
|October 11, 2024
PubMed
概括

因果随机森林 (CRF) 和其他机器学习方法可以评估暴露.

关键词:
因果贝叶斯增量回归树是因果贝叶斯增量回归树.因果随机森林的原因暴露于污染物的暴露.健康结果的健康结果.异质的治疗效果 异质的治疗效果基于树的机器学习工具.

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