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.
Aguilera R, Hansen K, Ilango S, Sheridan P, Gershunov A, Benmarhnia T. Spatio-temporal coherence between respiratory admissions and smoke plumes during the October 2007 wildfires in San Diego county. Environmental Epidemiology [In Press]
Ilango, S. D., Chen, H., Hystad, P., van Donkelaar, A., Kwong, J. C., Tu, K., & Benmarhnia, T. (2020). The role of cardiovascular disease in the relationship between air pollution and incident dementia: a population-based cohort study. International Journal of Epidemiology, 49(1), 36-44.
Benmarhnia T, Huang J, Wu J, Basu R, Bruckner T. A Decomposition Analysis of Black-White Disparities in Birth Outcomes: The Relative Contribution of Air Pollution and Social Factors in California. Environmental Health Perspectives: 125(10).