Miguel Hernán Miguel Hernán uses health data and causal inference methods to learn what works. As Director of the CAUSALab at Harvard, he and his collaborators repurpose real world data into scientific evidence for the prevention and treatment of infectious diseases, cancer, cardiovascular disease, and mental illness. This work has shaped health policy and research methodology worldwide. Miguel is co-director of the Laboratory for Early Psychosis (LEAP) Center, principal investigator of the HIV-CAUSAL Collaboration, and co-director of the VA-CAUSAL Methods Core, an initiative of the U.S. Veterans Health Administration to integrate high-quality data and explicitly causal methodologies in a nationwide learning health system. As the Kolokotrones Professor of Biostatistics and Epidemiology, he teaches at the Harvard T.H. Chan School of Public Health, where he has mentored dozens of trainees and students, and at the Harvard-MIT Division of Health Sciences and Technology. His free online course “Causal Diagrams” and book “Causal Inference: What If”, co-authored with James Robins, are widely used for the training of researchers. Miguel has received many awards for his work, including the Rousseeuw Prize for Statistics, the Rothman Epidemiology Prize, and a MERIT award from the U.S. National Institutes of Health. He is Fellow of the American Association for the Advancement of Science and the American Statistical Association, Associate Editor of Annals of Internal Medicine, Editor Emeritus of Epidemiology, and past Associate Editor of Biometrics, American Journal of Epidemiology, and Journal of the American Statistical Association. He often serves on committees of the U.S. National Academies.” Razieh NabiRazieh Nabi graduated from the Johns Hopkins University in 2021 with a PhD in Computer Science, subsequently joining Emory University where she is currently a Rollins Assistant Professor in the Department of Biostatistics and Bioinformatics, with a secondary appointment in Computer Science. Dr. Nabi’s research is at the intersection of statistics, machine learning, and public health, where she focuses on developing innovative causal inference methodologies. Her work tackles critical challenges such as unmeasured confounders in observational studies, the curse of dimensionality, and biases in data—especially informative missing and censored data, and those reflecting historical patterns of discrimination and inequality. Her methodologies have profound implications across various fields including social justice, public policy, and healthcare research.