Causal Mediation Analysis Training

Methods and Applications Using Health Data
10:00 am
5:00 pm
Tuesday
13
July
2021
Add to Calendar:
Virtual, Live-stream
Linda Valeri, PhD, Caleb Miles, PhD
Linda Valeri, PhD, Assistant Professor of Biostatistics; Caleb Miles, PhD
Training
Department of Environmental Health Sciences
SHARP Training Program
Open to the Public
This three-day intensive course will cover some of the recent developments in causal mediation analysis and provide practical tools to implement these techniques and assess the mechanisms and pathways by which causal effects operate. Led by a team of experts in causal mediation techniques at Columbia University, this course will integrate lectures and discussion with hands-on computer lab sessions using R. The course will cover the relationship between traditional methods for mediation in environmental health, epidemiology, and the social sciences and new methods in causal inference using a wide variety of examples to illustrate the techniques and approaches.


By the end of the workshop, participants will be familiar with the following topics:

-Understand when traditional methods for mediation fail
-Articulate concepts about mediation under the counterfactual framework and assumptions for identification
-Formulate and apply regression approaches for mediation for single and multiple mediators
-Develop facility with the use of software for mediation and interpretation of software output

Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. The Training will be a live-stream, remote training that takes place over live, online video. Please note this training is not a self-paced, pre-recorded online training.


PREREQUISITES AND REQUIREMENTS

-Each participant must be familiar with linear and logistic regression.
-Each participant must have experience with programming in R.
-Although the instructors will provide an overview of the fundamentals of causal inference (potential outcomes, directed acyclic graphs, and marginal structural models), we invite the participants to read chapters 1-7, 11, and 12 of Hernan MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC (free).
-Each participant is required to bring a personal laptop with R/RStudio installed prior to the first day of the workshop, as all lab sessions will be done on your personal laptop. More information about R can be found on the training website.


INSTRUCTORS

Linda Valeri, PhD, Department of Biostatistics, Mailman School of Public Health, Columbia University.

Caleb Miles, PhD, Department of Biostatistics, Mailman School of Public Health, Columbia University.


ADDITIONAL INFORMATION
- Scholarships are available: https://www.publichealth.columbia.edu/research/precision-prevention/professional-development-scholarships
- Subscribe for updates: http://eepurl.com/dLbQkA
- Email our team: Columbia.CMA@gmail.com.

Capacity is limited. Paid Registration is required to attend.

Contact:

Casual Mediation Analysis