Causal inference is a central aim of many quantitative sciences including epidemiology. We aim at applying and developing different causal inference methods in the context of environmental health issues with a focus on the role of social exposures. Our work includes for example matching techniques, mediation analyses, marginal structural models or transportability methods.
- Bai, L., Benmarhnia, T., Chen, C., Kwong, J. C., Burnett, R. T., van Donkelaar, A., ... & Chen, H. (2022). Chronic Exposure to Fine Particulate Matter Increases Mortality Through Pathways of Metabolic and Cardiovascular Disease: Insights From a Large Mediation Analysis. Journal of the American Heart Association, 11(22).
- Chen, H., Quick, M., Kaufman, J. S., Chen, C., Kwong, J. C., van Donkelaar, A., ... & Burnett, R. T. (2022). Impact of lowering fine particulate matter from major emission sources on mortality in Canada: A nationwide causal analysis. Proceedings of the National Academy of Sciences, 119(49).
- Sheridan, P., McElroy, S., Casey, J., & Benmarhnia, T. (2022). Using the generalized synthetic control method to estimate the impact of extreme weather events on population health. Epidemiology, 33(6), 788-796.