We all use the phrase cause and effect, but do we really know what it means and how it applies to our daily lives?
Two professors in USF鈥檚 (COPH) are promoting the idea in their Causal Inference in Public Health Research (CIPHR) Lab, which focuses on its application, evaluation and development in the context of public health.
鈥淎 lot of research questions concern cause and effect,鈥欌€� said , assistant professor in the COPH . 鈥淔or example, questions like 鈥榙oes alcohol consumption cause liver disease鈥� or 鈥榙oes vaping lead to lung injury鈥� are questions about cause and effect, and the answers can have important implications for medical and public health policies.鈥欌€�

Dr. Judith Rijnhart presents at an event. (Photo courtesy of Vishkaha Agrawal)
Rijnhart, who specializes in the evaluation and application of methods for statistical mediation analysis, created the lab in 2024 with , assistant professor in the and a quantitative psychologist. The lab provides students with a comprehensive set of research skills, including data analysis, manuscript writing and presentation skills.
The lab includes 14 master and doctoral students with concentrations in epidemiology and biostatistics who recently presented their work at the USF Health Research Day, an annual event showcasing the best and the brightest researchers across all USF Health colleges.

Dr. Judith Rijnhart (Photo courtesy of Valente)
鈥淲e created the lab to not only train students in causal inference methods, but also essential research skills,鈥欌€� Rijnhart said. 鈥淎nd we鈥檙e interested in bringing together students with different educational and research backgrounds.鈥欌€�
Causal inference in public health is a branch of research that helps determine if associations between factors and health outcomes are actually causal, enabling informed interventions and policy decisions. It involves rigorous methods and assumptions to establish whether a treatment or exposure truly leads to a specific effect.
鈥淚t forces you to think critically and become a detective who investigates what scenarios most likely explain the observed statistical association,鈥欌€� Valente said. 鈥淪o, anyone would benefit from understanding its principles.鈥欌€�

Dr. Matthew Valente (Photo courtesy of Valente)
Emily Parow, a COPH student in epidemiology, said the lab has given her a stronger foundation in both causal inference and research methodology, reflected in her improved scientific writing and confidence as a public health researcher. She currently is leading a project examining how occupational differences by gender affect dementia status.
鈥淭his experience has allowed me to be hands-on throughout the entire research process, from the literature review stage to data analysis, while receiving feedback from peers in the lab,鈥欌€� she said. 鈥淐IPHR has truly allowed me to pursue my passion for research and build expertise in methods that are critical to the future of public health 鈥� it鈥檚 inspired me to pursue a PhD after completing my master鈥檚 degree.鈥欌€�
The CIPHR lab helps students to:
- Identify causal associations 鈥� Understanding whether an association between a variable and a health outcome is truly causal is crucial in developing effective public health interventions.
- Make more informed public health policies 鈭� The findings can inform policy decisions by providing evidence-based recommendations for interventions and prevention strategies.
- Advance scientific understanding 鈭� The lab contributes to the advancement of methodological tools and knowledge in causal inference, helping researchers to better understand complex health phenomena.
鈥淎n association doesn鈥檛 always mean one thing causes the other, and the lab has helped me answer the deeper question: Does this factor actually cause an outcome?鈥欌€� said Biwei Cao, a PhD student in biostatistics. 鈥淲orking at a cancer center, I鈥檝e seen how important it is to go beyond just finding things that are linked to cancer and instead figure out what actually causes it. This kind of analysis opened up a whole new way for me to think about data and its real-world implications.鈥欌€�