A Machine-Learning Algorithm for Estimating and Ranking the Impact of Environmental Risk Factors in Exploratory Epidemiological Studies

Jessica G. Young (), Alan E. Hubbard (), Brenda Eskenazi () and Nicholas P. Jewell ()
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Jessica G. Young: Harvard University, Department of Population Medicine, Harvard Medical School & Harvard Pilgrim Health Care Institute
Alan E. Hubbard: University of California at Berkeley, Division of Biostatistics
Brenda Eskenazi: University of California at Berkeley, Division of Environmental Health Sciences, School of Public Health
Nicholas P. Jewell: University of California at Berkeley, Division of Biostatistics

A chapter in Statistical Modeling for Biological Systems, 2020, pp 137-156 from Springer

Abstract: Abstract Epidemiological research, such as the identification of disease risks attributable to environmental chemical exposures, is often hampered by small population effects, large measurement error, and limited a priori knowledge regarding the complex relationships between the many chemicals under study. However, even an ideal study design does not preclude the possibility of reported false positive exposure effects due to inappropriate statistical methodology. Three issues often overlooked include (1) definition of a meaningful measure of association; (2) use of model estimation strategies (such as machine-learning) that acknowledge that the true data-generating model is unknown; (3) accounting for multiple testing. In this paper, we propose an algorithm designed to address each of these limitations in turn by combining recent advances in the causal inference and multiple-testing literature along with modifications to traditional nonparametric inference methods.

Keywords: Machine-learning; Epidemiology; Multiple testing; Causal inference (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-34675-1_8

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DOI: 10.1007/978-3-030-34675-1_8

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