Paul Zivich
About
Paul is an assistant professor in the Department of Epidemiology at University of North Carolina at Chapel Hill. His interests are in causal inference with potential outcomes, with specialization in the context of networks and contagious outcomes (e.g. infectious diseases, health behaviors, etc.). Dr. Zivich also focuses on computational aspects of epidemiology. His work has ranged from assessing the performance of estimators through simulation studies to free and open source software (FOSS) to collection of contact network data with electronic sensors to application of causal inference in the context of infectious disease and social epidemiology.
Paul received his PhD in epidemiology from University of North Carolina at Chapel Hill, and his MPH in epidemiology from The Ohio State University. He was born, is conducting research, and will die.
This website provides a highlighted selection of publications, presentations, and software I have written. For a full summary of all my previous work, please see my CV (linked below).
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To adhere to the FAIR (Findability, Accessibility, Interoperability, and Reuse) principles for my research software, my publication code is available long-term on Zenodo.
Current Research
The following is a brief summary of my ongoing research projects or current research interests.
Non-standard causal inference
Within epidemiology, the assumption of causal consistency, exchangeability, and positivity are often relied on for identification of causal effects. My interests lie in how we can revise or avoid these assumptions. Examples of my work in this area include proximal causal inference, causal inference with interference, fusion designs, machine learning, and semi-empirical modeling.
Estimating equations
A set of powerful tools for statistical analyses are estimating equations. An ongoing interest is in application of these tools for epidemiologic analyses and to ease their adoption by epidemiologists. My main contribution in this area has been the development of delicatessen, a Python library for general application of estimating equations.
Other examples of my work in this area include M-estimators for bridged treatment comparisons, M-estimators for fusion designs, M-estimators for sensitivity analyses, and M-estimators for g-computation.
Infectious diseases
My applied work focuses on infectious disease epidemiology. My main interest is how observational studies on vaccine effectiveness can be improved. Previous and ongoing research have focused on influenza, pneumonia, HIV, SARS-CoV-2, testing, and vaccines.