Causal Mediation Analysis at Columbia University 2025

Mediation analysis is an emerging field in causal inference relevant for comparative effectiveness research, evaluating and improving policy recommendations, and explaining biological mechanisms. Training in the potential outcomes framework for causal inference is important to understand the assumptions required for valid mediation analyses. This course will equip participants with foundational concepts and cutting edge statistical tools to investigate mediating mechanisms.

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. We will discuss 1) when the standard approaches to mediation analysis are valid for dichotomous, and continuous, outcomes, 2) alternative mediation analysis techniques when the standard approaches will not work, introducing the counterfactual notation for mediation analysis and formal definitions of natural direct and indirect effects, 3) the no-unmeasured confounding assumptions needed to identify these effects, and 4) how regression approaches for mediation analysis can be extended in the presence of multiple mediators.

Table of Content

• Summary
• Benefits
• Requirments
• Application Deadline
• How To Apply

Summary

• Application DeadlineJuly 11, 2025
• Study LevelTraining
• SponsorColumbia University
• City to studyNew York

Subscribe for Scholarship Alert!

Benefits

Master causal mediation analysis in a rigorous three-day boot camp. Engage in seminars and hands-on sessions to explore mediating mechanisms.

Requirements

Investigators from any institution and from all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are four requirements to attend this training:

1. Each participant must be familiar with linear and logistic regression.
2. Each participant must have experience with programming in R.
3. 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 Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC (free).
4. Each participant is required to have a personal laptop/computer and a free, basic Posit Cloud (formerly RStudio Cloud) account. All lab sessions will be done using Posit Cloud (formerly RStudio Cloud).

Application Deadline

July 11, 2025

How To Apply

Are you qualified and interested in this opportunity? Kindly go to Columbia University on columbia-workshops.regfox.com to apply

• 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

For more details visit Columbia University Scholarship Webpage