Causal Analysis in Theory and Practice

We are informed of the following short course  at Harvard. Readers of this blog will probably wonder what this Harvard-specific jargon is all about, and whether it has a straightforward translation into Structural Causal Models. It has! And one of the challengesof contemporary causal inference is to navigate the literature despite its seeming diversity, and to work towards convergence of ideas, tools and terminology.

Summer Short Course “An Introduction to Causal Inference”

Date: June 3-7, 2019

Instructors: Miguel Hernán, Judith Lok, James Robins, Eric Tchetgen Tchetgen & Tyler VanderWeele

This 5-day course introduces concepts and methods for causal inference from observational data. Upon completion of the course, participants will be prepared to further explore the causal inference literature. Topics covered include the g-formula, inverse probability weighting of marginal structural models, g-estimation of structural nested models, causal mediation analysis, and methods to handle unmeasured confounding. The last day will end with a “capstone” open Q&A session with the instructors.

Prerequisites: Participants are expected to be familiar with basic concepts in epidemiology and biostatistics, including linear and logistic regression and survival analysis techniques.

Tuition: $600/person, to be paid at the time of registration. A limited number of tuition waivers are available for students.

Date/Location: June 3-7, 2019 at the Harvard T.H. Chan School of Public Health. 

Details and registration:https://www.hsph.harvard.edu/causal/shortcourse/