Using casual inference and machine learning with exposure determinant modeling to identify important workplace controls
Our causal inference approach with machine learning algorithms overcomes the common limitations of exposure determinant modeling and produces easy-to-interpret estimates of biomarker concentration reductions from hypothetical workplace controls. This can aid industrial hygienists in choosing the most effective hazard controls that can be contextualized to their particular work setting.
Authors
- Pablo Ruiz-Rudolph (ORCID: https://orcid.org/0000-0001-7872-2546)
- Kowit Nambunmee (ORCID: https://orcid.org/0000-0002-6629-7316)
- Abas Shkembi (ORCID: https://orcid.org/0000-0002-5398-861X)
- Richard L. Neitzel (ORCID: https://orcid.org/0000-0001-5500-2589)
- Jie He
- Mohammed Abbas Virji
Institutions
- Universidad de Santiago de Chile (CL)
- West Virginia University (US)
- Michigan Department of Health and Human Services (US)
- University of Michigan (US)
- Mae Fah Luang University (TH)
Publication Details
- Journal
- Annals of Work Exposures and Health
- Published
- 2025-10-03
- DOI
- https://doi.org/10.1093/annweh/wxaf069
- Citations
- 1
- Primary Topic
- Recycling and Waste Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.00