Causal Inference

Overview

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.

Select Publications

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.