Member of the Faculty, Harvard-MIT Division of Health Sciences & Technology
Associate Member, Broad Institute
Gästprofessor i epidemiologi, Karolinska Institutet
Data Analytics Collaborator, Veterans Administration Boston Healthcare System
Special Government Employee, U.S. Food and Drug Administration
Data Science Adviser, ProPublica
My research is focused on learning 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 individuals living 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.
If you wish to learn more about methodological aspects of my research, click on the items below for a guided tour of select publications.
The interplay between causal inference and machine learning is of great interest to me. You can watch me debate about this topic here. If you prefer podcasts, click here for my views on causal inference from big healthcare databases and here for a discussion on why good science requires the use of explicitly causal language. 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 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.