Machine Learning

*Seminal work by Brieman on the distinction between a more classical statistical modeling approach and more recent “algorithmic” modeling approach.  Leo Breiman. “Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author).” Statist. Sci. 16 (3) 199 – 231, August 2001.

https://doi.org/10.1214/ss/1009213726

Excellent introduction to concepts and issues in using machine learning for epidemiologists. Bi Q, Goodman KE, Kaminsky J, Lessler J. What is Machine Learning? A Primer for the Epidemiologist. Am J Epidemiol. 2019;188(12):2222-2239.

https://academic.oup.com/aje/article/188/12/2222/5567515?login=true

Attempt to demonstrate the fundamentals behind the super learner. Naimi AI, Balzer LB. Stacked generalization: an introduction to super learning. Eur J Epidemiol. 2018;33(5):459-464.

https://pubmed.ncbi.nlm.nih.gov/29637384/

Detailed resource on using the super learner in real data settings.  Kennedy, C. “Guide to SuperLearner.” (2017).

https://cran.r-project.org/web/packages/SuperLearner/vignettes/Guide-to-SuperLearner.html

Important example of some fundamental constraints on using data with algorithms to predict outcomes fairly.  Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5(2), 153-163.

https://arxiv.org/abs/1703.00056

Excellent introduction to machine learning (emphasis on econometrics but very useful for epidemiologists).  Mullainathan, S. and J. Spiess, Machine learning: an applied econometric approach. Journal of Economic Perspectives, 2017. 31(2): p. 87-106

https://www.aeaweb.org/articles?id=10.1257/jep.31.2.87

Important example of how ML algorithms can yield very misleading predictions when deeper aspects of the data-modeling complex are not taken into account.  Caruana, R., et al. Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day .Readmission in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015. ACM.