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Estimating population health impacts from observational data



Full course description


This course provides an overview of how to evaluate the effect of a hypothetical intervention on outcomes using observational data. Participants will review key epidemiologic concepts and analyses including directed acyclic graphs (DAGs), counterfactuals, measures of association, and linear and logistic regression before learning the steps of marginal standardization, including coding example problems in R. There is an emphasis on how to apply these tools to answer real-world questions, such as:

·       Estimating the effect of different durations of daily exercise on the probability of being overweight among a cohort of adults using observational data


The course begins May 15, 2024


Intended Audience

This course is targeted to those who have introductory-level training in epidemiology and biostatistics as well as experience using logistic regression, but it is open to all regardless of previous experience.


Time commitment

This 4.5-hour course is a combination of lecture and coding in R, including a 1-hour self-directed coding activity in R.



Upon completion of all videos, quizzes and activities, participants will be awarded a certificate of completion.  


About the course facilitator

Stephanie Veazie is an Epidemiology PhD student at the University of California-Berkeley whose research focuses on how social determinants of health drive inequities in maternal and child health and mental health. She earned an MPH from the University of Wisconsin in 2014 and a BS in Psychology from the University of Illinois in 2008. Prior to starting her PhD, Stephanie worked as a Research Associate on several studies pertaining

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