Dr. Miguel Hernán stands as a preeminent figure in the field of causal inference methodology within public health and epidemiology. He currently holds the distinguished position of Kolokotrones Professor of Biostatistics and Epidemiology at the Harvard T.H. Chan School of Public Health, where he also serves as Director of the groundbreaking CAUSALab research initiative. After completing his medical degree at Universidad Autónoma de Madrid in 1995, he pursued advanced public health training at Harvard, earning both an MPH and ScM in Biostatistics in 1995 and 1999 respectively, followed by a DrPH in Epidemiology. His career trajectory at Harvard began in 1999, progressing to full professorship in 2011 and culminating in his prestigious named professorship in 2016, reflecting his extraordinary contributions to methodological research in public health.
Dr. Hernán has fundamentally transformed how researchers approach causal questions in observational data through his pioneering development of causal inference frameworks that emulate randomized experiments. His seminal work, including the influential textbook "Causal Inference: What If" co-authored with James Robins, has established methodological standards that guide researchers worldwide in properly addressing confounding and selection bias in health studies. With Google Scholar citations exceeding 110,000, his approaches have been widely adopted to evaluate interventions for infectious diseases, cancer, cardiovascular conditions, and mental health disorders using real-world data. His methodological innovations have directly informed health policy decisions and clinical practice guidelines by providing rigorous evidence where randomized trials are impractical or unethical, particularly through his leadership of the HIV-CAUSAL Collaboration and the VA-CAUSAL Methods Core initiatives.
Beyond his technical contributions, Dr. Hernán has shaped an entire generation of epidemiologists and biostatisticians through his widely accessed educational resources, including his free online course "Causal Diagrams" that has trained researchers globally. He currently serves as Associate Editor of the Annals of Internal Medicine and previously held editorial leadership positions at Epidemiology, Biometrics, and the American Journal of Epidemiology, where he championed methodological rigor in published research. His laboratory continues to advance causal inference methods to address pressing public health challenges, particularly in repurposing real-world healthcare data to generate evidence for clinical decision-making during rapidly evolving health crises. As co-director of the Laboratory for Early Psychosis Center and through his numerous institutional leadership roles, Dr. Hernán remains committed to bridging the gap between methodological innovation and practical health applications to improve patient outcomes worldwide.
