Thirteen Questions About Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!)

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.

Automated summary

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.