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In Pursuit of Evidence in Air Pollution Epidemiology

The Role of Causally Driven Data Science

Carone, Marco1; Dominici, Francesca2; Sheppard, Lianne1,3

doi: 10.1097/EDE.0000000000001090
Commentary: PDF Only

1 Department of Biostatistics, University of Washington

2 Department of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University

3 Department of Environmental and Occupational Health Sciences, University of Washington

Sources of financial support: Drs. Sheppard and Carone were supported by NIH grant R01 ES026187. Dr. Dominici was supported by the Health Effects Institute (HEI), an organization jointly funded by the United States Environmental Protection Agency (EPA) (Assistance Award No.CR-83467701) and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of HEI, or its sponsors, nor do they necessarily reflect the views and policies of the EPA or motor vehicle and engine manufacturers.

Conflict of interest: none declared

Acknowledgments: This commentary was originally presented to kick off the discussion at the “Causal Modeling in Air Pollution Research and Policy” pre-conference workshop at the 2018 HEI Annual Conference. The authors wish to thank Katherine Walker, Sverre Vedal, Rachel Shaffer, Patrick Heagerty and Mark van der Laan for helpful comments on earlier drafts of this work, and three anonymous referees for their constructive feedback.

Correspondence: Lianne Sheppard, PhD, Box 357232, Department of Biostatistics, University of Washington, Seattle, WA 98195-7232 (tel: 206 616-2722; email:

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