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Columbia University: Causal Mediation Analysis Training: Methods and Applications Using Health Data
Date/Time: Jul 09, 2025, 08:00 AM to Jul 11, 2025, 05:00 PM
Location: Livestream, virtual training
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.
By the end of the workshop, participants will be able to:
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