Miguel Hernán
Director at Adigens Health
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolMiguel Hernán is a leading biostatistician and epidemiologist who directs CAUSALab and holds the Kolokotrones Professorship at Harvard T.H. Chan School of Public Health, applying causal inference to turn real-world data into actionable evidence for infectious disease, cancer, cardiovascular and mental health policy. He bridges academia, government, and industry—co-directing national VA and HIV research initiatives, serving as Methods Editor at Annals of Internal Medicine, and co-founding Adigens Health to translate methods into practice. His teaching and widely used resources, including the free “Causal Diagrams” course and the book “Causal Inference: What If,” have shaped how researchers reason about causality. A decorated scholar and frequent advisor to U.S. agencies and journals, he combines methodological rigor with tangible policy impact. An MD by training with advanced public health and biostatistics degrees from Harvard, he is notable for converting complex causal methods into pragmatic tools for learning health systems.
8 years of coding experience
19 years of employment as a software developer
Licenciado en Medicina - MD, Licenciado en Medicina - MD at Universidad Autónoma de MadridDoctor of Public Health, Epidemiology, Doctor of Public Health, Epidemiology at Harvard T.H. Chan School of Public Health
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
estimate10
principal-component-analysis9
sas9
longitudinal-data9
statsmodels8
parametric7
economics6
econometrics5
Programming languages (1)
Github contributions (2)
CausalInference/CompetingEvents_Young_SIM_2020
Jan 2020 - Jan 2020
Contributions:6 commits, 5 pushes, 1 branch in 1 day
CausalInference/GFORMULA-SAS
Aug 2017 - Aug 2021
The GFORMULA macro implements the parametric g-formula in SAS. The parametric g-formula (Robins, 1986) uses longitudinal data with time-varying treatments and confounders to estimate the risk or mean of an outcome under hypothetical treatment strategies specified by the user.
Contributions:8 commits, 7 pushes, 1 branch in 4 years
time-varyingprincipal-component-analysisoutcomeparametricrisk
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