Lucia C. Petito, PhD, is a biostatistician who specializes in causal inference, using data from both clinical trials and observational sources such as health records and insurance claims. She is an expert in the target trial framework, an approach that applies the design principles of randomized trials to the analysis of observational data. Her work pairs methodological innovation with pressing clinical questions, with the goal of generating evidence to guide medical decision-making. She develops and extends causal inference methods and partners with clinicians to apply them to questions about medication safety, treatment effectiveness, and the impact of policy and lifestyle interventions. Dr. Petito provides statistical leadership for clinical trials and observational studies across many areas of medicine, including cardiology, endocrinology, pediatrics, primary care, obstetrics, and oncology.

Dr. Petito is an Associate Professor in the Department of Public Health Sciences at the University of Chicago. Before joining UChicago, she was a faculty member in the Department of Preventive Medicine at Northwestern University’s Feinberg School of Medicine from 2019 to 2026. She earned her PhD in biostatistics from the University of California, Berkeley, in 2017 and then completed a two-year postdoctoral fellowship at CAUSALab at the Harvard T.H. Chan School of Public Health. Her postdoctoral work with Miguel Hernán was among the first to emulate target trials using the SEER-Medicare linked database. By benchmarking these emulations against previously published clinical trials, the work showed that the database contained enough information to adequately control for confounding.

Dr. Petito’s current collaborations include comparative effectiveness studies using real-world data funded by the National Institutes of Health (NIH) and the Department of Defense. These projects examine guideline-concordant care for pediatric sepsis, blood pressure targets in heart failure, treatment of anemia in pregnancy, and treatment of metastatic hormone-sensitive prostate cancer. She also has deep expertise in designing and analyzing pragmatic clinical trials. Ongoing NIH-funded trials include the High-Risk Polypharmacy (HRPP) trial, a pragmatic, clinic-randomized 2x2 factorial trial testing two types of clinical decision support tools designed to reduce high-risk prescribing in primary care, and the AUTO-2 trial, a patient-randomized 2x2 factorial trial comparing manually recorded versus automatically transmitted home blood pressure readings, with or without automated feedback to patients.

Dr. Petito has developed several introductory tutorials and workshops on causal inference concepts, and she regularly mentors students and early-career colleagues.