Introduction to Directed Acyclic Graphs (DAGs) for Causal Inference Training
Wednesday, February 26, 2020
10:00 AM-12:00 PM
The Dornsife School of Public Health's Urban Health Collaborative and Research Office present this training that provides an introduction to DAG.
DAGs are diagrams used to represent causal questions. They serve as a
visual aid to summarize assumptions about causal and non-causal
associations between a given exposure and an outcome. DAGs are used to
identify variables that must be measured and controlled for in order to
obtain unconfounded effect estimates.
At the end of the training session, participants should be able to:
- Recognize the essential elements of a DAG
- Identify a collider variable
- Use d-separation criteria to condition models on a set of adequate confounders
- Draw a DAG and identify a minimal sufficient adjustment set of confounders using DAGitty interactive tool
Mullachery, PhD, MPH, postdoctoral research fellow, Urban Health
Collaborative; and Ione Avila-Palencia, PhD, MPH, postdoctoral research
fellow, Urban Health Collaborative.
Please RSVP by February 20 by emailing firstname.lastname@example.org and please indicate if you plan to bring a laptop with you or would like us to provide one.