Causal Diagrams: From Assumptions to Conclusions

Miguel Hernán, Kolokotrones Professor of Biostatistics and Epidemiology at Harvard T.H. Chan School of Public Health, has transformed epidemiological research through pioneering work in causal inference methodology. After earning his medical degree from Universidad Autónoma de Madrid and multiple graduate degrees from Harvard, including a DrPH in Epidemiology, he has built an extraordinary career combining methodological innovation with practical applications. As Director of the CAUSALab at Harvard, he leads groundbreaking research using health data and causal inference methods to improve treatment and prevention of diseases ranging from HIV to cancer. His free online course "Causal Diagrams" has reached over 80,000 learners, while his co-authored book "Causal Inference: What If" has become a cornerstone text in the field. His achievements include the 2022 Rousseeuw Prize for Statistics, election as Fellow of both the American Association for the Advancement of Science and American Statistical Association, and a MERIT Award from the National Institutes of Health. Through his roles at Harvard-MIT Health Sciences and Technology and as Methods Editor for Annals of Internal Medicine, he continues to advance causal inference methodology while mentoring the next generation of researchers.