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Paul Zivich


Paul Zivich is interested in causal inference with potential outcomes, particularly in the context of networks and contagious outcomes (e.g. infectious diseases, health behaviors). His work ranges from assessing the performance of causal inference estimators through simulations to collection of contact network data with electronic sensors to application of causal inference in the context of infectious disease and social epidemiology. He is also the author of the zEpid Python library, a free-software library for epidemiologic analyses and quantitative causal inference.

Research Interests

Causal inference; social network analysis; respiratory infectious diseases; vaccines; statistical software.

Faculty Preceptor(s)

James Moody, Allison Aiello