Open AccessThirteen Questions About Using Machine Learning in Causal Research (You Won’t Believe the Answer to Number 10!)
Author(s) -
Stephen J. Mooney,
Alexander P. Keil,
Daniel Westreich
Publication year - 2021
Publication title -
american journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 256
eISSN - 1476-6256
pISSN - 0002-9262
DOI - 10.1093/aje/kwab047
Subject(s) - machine learning , computer science , artificial intelligence , causal inference , black box , data science , code (set theory) , econometrics , mathematics , programming language , set (abstract data type)
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.