Causal Mediation Analysis Training: Methods and Applications Using Health Data
The Causal Mediation Analysis Training is a 3-day intensive boot camp of seminars and hands-on analytical sessions to provide an overview of concepts and data analysis methods used 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, using ideas from causal inference and 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:
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Understand when traditional methods for mediation fail
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Articulate concepts about mediation under the counterfactual framework and assumptions for identification
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Formulate and apply regression approaches for mediation for single and multiple mediators
Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate.
PREREQUISITES AND REQUIREMENTS
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Each participant must be familiar with linear and logistic regression.
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Each participant must have experience with programming in R.
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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).
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Each participant is required to have a personal computer and a free, basic RStudio Cloud account. All lab sessions will be done using RStudio Cloud.
ADDITIONAL INFORMATION
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Email us at [email protected].
Capacity is limited. Paid registration is required to attend.
