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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