Paul Zivich
About
Dr. Paul Zivich is a research assistant professor in the Department of Epidemiology at University of North Carolina at Chapel Hill. His interests are in causal inference with potential outcomes, infectious disease prevention and 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, code from selected publications is available on Zenodo.
Explainers
Current Research
The following is a brief summary of my ongoing research projects or current research interests.
Non-standard causal inference
When first taught causal inference, many associate it directly with rote memorization of the standard identification assumptions of causal consistency, exchangeability, and positivity. Others associate it with specific estimation methods (e.g., inverse probability weighting). However, causal inference is so much more.
One major area of my on-going research is in these ‘non-standard’ areas of causal inference. Examples of my work in this area include: synthesis modeling to address violations of the positivity assumption, use of proximal causal inference to account for unmeasured confounding variables, data fusion to integrate information across data sources, machine learning to weaken modeling assumptions, and causal inference with interference.
Estimating equations
During my postdoc, I began to study estimating equations. After understanding this approach, it has changed how I approach my research. I have found estimating equations to be a set of powerful tools for statistical analyses. This has simplified by methodological work (by making proofs on statistical properties simpler) and applied work (by allowing me to avoid bootstrapping). My ongoing interest is in furthering these tools for epidemiologic analyses and to ease their adoption by epidemiologists.
My primary 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 introductory materials developed for epidemiologists, extensions for g-computation with marginal structural models or time-varying confounding, use with sensitivity analyses, and fusion designs generally and for bridged treatment comparisons.
Infectious diseases
My applied work focuses mainly on infectious disease epidemiology. Previous and ongoing research have focused on HIV, influenza, pneumonia, SARS-CoV-2, testing strategies, and vaccines.