Member of the Faculty, Harvard-MIT Division of Health Sciences & Technology
Associate Member, Broad Institute
Special Government Employee, U.S. Food and Drug Administration
I do research to learn what works to improve human health. My collaborators and I design analyses of healthcare databases, epidemiologic studies, and randomized trials. We generate and analyze data to identify better strategies for the treatment and prevention of both infectious and noninfectious diseases. I serve as
- Principal Investigator of the HIV-CAUSAL Collaboration, a multinational consortia of prospective studies from Europe and the Americas. We conduct comparative effectiveness research for the treatment of persons with HIV.
- Co-Director of the Laboratory for Early Psychosis (LEAP) Center, a joint collaboration with McLean Hospital and the Massachusetts General Hospital. Our goal is to better understand the clinical course and care options for individuals with first episode psychosis.
- Co-Director of the VA-CAUSAL Methods Core, a causal inference research initiative within the Veterans Health Administration to help transform the VA into a learning health system that expedites the translation of research into practice and supports decision-making by patients, clinicians, and other stakeholders.
Causal inference from observational data can be viewed as an attempt to emulate a (hypothetical) randomized trial—the target trial. You can read this or this for an introduction to the concept of target trial. If you wish to learn more about methodological aspects of my research, click on the items below for a guided tour of select publications.
If you like podcasts, click below to hear my views on causal inference and other issues:
- Observational data to inform public health and clinical care decisions, New England Journal of Medicine Interviews
- Why good science requires the use of explicitly causal language, American Journal of Public Health Podcast
- Pandemics in the USA, China, and Spain a century ago vs today, American Journal of Public Health Podcast
- Big data and public health, Harvard Chan THIS WEEK
- Talking Target Trials with Miguel Hernán, Casual Inference Podcast
If you prefer to listen to me in person, I plan to participate in these scientific meetings.
My teaching is focused on how to generate, analyze, and interpret data to guide health policy and clinical decisions. I teach causal inference methodology at the Harvard T.H. Chan School of Public Health, and clinical epidemiology at the Harvard-MIT Division of Health Sciences and Technology. For more info about my teaching, click here.
For anyone interested in causal inference, we have put together a few free resources:
- Causal Inference: What If book
- HarvardX course Causal Diagrams: Draw Your Assumptions Before Your Conclusions
- Open source software for causal inference. Also, here
Also, if you can bear with me, I tweet as @_MiguelHernan about data science and causal inference.