Courses Taught
Causal Inference - EPI289
Instructor: Miguel Hernán
Causal inference from
observational data is a key task of epidemiology and of allied sciences
such as sociology, education, behavioral sciences, demography,
economics, health services research, etc. These disciplines share a
methodological framework for causal inference that has been developed
over the last decades.
EPI289 presents this unifying causal theory and shows how epidemiologic
concepts and methods introduced in EPI201 and EPI202 can be understood
within this general framework. The course emphasizes conceptualization
but also introduces statistical models and methods for time-varying
exposures, which will be extensively studied in EPI204 and EPI207.
Spring Semester, Harvard School of Public Health
EPI289 web site
Advanced Epidemiologic Methods - EPI207
Instructors: James Robins and Miguel Hernán
This course provides an in depth investigation of statistical
methods for drawing causal inferences from observational studies.
Informal epidemiologic concepts such as confounding, comparability,
overall effects, direct effects, intermediate variables, and selection
bias are formally defined within the context of a counterfactual causal
model. Methods for the analysis of the causal effects of time-varying
exposures in the presence of time dependent covariates that are
simultaneously confounders and intermediate variables are emphasized.
These methods include g-estimation of structural nested models, inverse
probability weighted estimators of marginal structural models, and
g-computation algorithm estimators. As a practicum, students reanalyze
data sets using the above methods.
Fall semester, Harvard School of Public Health
EPI207 web site
Introduction to Biostatistics and Epidemiology - HST190/191
Instructors: Rebecca Betensky, Miguel Hernán, Yves Chretien, Christina Mills
This course presents
the fundamentals of biostatistics and epidemiology with the aim of training
students to comprehend, critique and communicate findings from the biomedical
literature. In the first part of this course, students will learn how to assess
the importance of chance in the interpretation of experimental data. Major
topics covered include probability theory, normal sampling, chi-squared and
t-tests, analysis of variance, linear regression and survival analysis, as well
has how to perform elementary calculations using the statistical package STATA.
In the second part of this course, students will learn how to identify and prevent
bias in observational studies. Students will learn about causal inference,
types of bias (confounding, selection and measurement bias), and key study
designs (randomized trials, cohort studies and case-control studies).
Winter IAP, Harvard-MIT Division of Health Sciences and Technology
HST190 web site