期刊
AMERICAN JOURNAL OF EPIDEMIOLOGY
卷 190, 期 8, 页码 1476-1482出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwab047
关键词
causal inference; double-robustness; epidemiologic methods; inverse probability weighting; machine learning; propensity score; targeted maximum likelihood estimation
资金
- National Library of Medicine [K99012868]
- National Institute of Environmental Health Sciences [R01ES029531]
- Office of the Director of the National Institutes of Health [DP2HD084070]
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
Machine learning is increasingly used in health sciences, not only for data-driven prediction but also for embedding within causal analyses to reduce biases. An introduction and orientation provided in a question-and-answer format can help epidemiologists interested in using machine learning techniques to overcome potential bias and maintain rigor. Sample software code is also available to facilitate entry into using these techniques.
Machine learning is gaining prominence in the health sciences, where much of its use has focused on data-driven prediction. However, machine learning can also be embedded within causal analyses, potentially reducing biases arising from model misspecification. Using a question-and-answer format, we provide an introduction and orientation for epidemiologists interested in using machine learning but concerned about potential bias or loss of rigor due to use of black box models. We conclude with sample software code that may lower the barrier to entry to using these techniques.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据