GESIS Training Courses

Scientific Coordination

Alisa Remizova

Administrative Coordination

Janina Götsche

Causal Mediation Analysis

Online via Zoom
General Topics:
Course Level:
Software used:
5 days
Students: 220 €
Academics: 330 €
Commercial: 660 €
Additional links
Lecturer(s): Felix Thoemmes

About the lecturer - Felix Thoemmes

Course description

Mediation analysis has been used by social scientists for the last 50 years to explain intermediate mechanisms between an assumed cause and effect. During these years, many advances in statistical mediation analyses were made, including the use of multiple mediators, models for limited dependent variables, latent variable modeling, improved standard errors, and the combination of mediation and moderation analysis. However, only very recently were the causal foundations and underlying assumptions of mediation analysis clarified. These more recent advances use potential outcomes notation and graphical causal models to illuminate the types of causal effects that can be estimated and, more importantly, which assumptions are needed to recover an unbiased causal effect. This workshop will offer a comprehensive overview of causal mediation analysis. It will include a review of potential outcomes notation and the structure of directed acyclic graphs. Following, students will learn the basic definitions of causal mediation effects, their underlying assumptions and estimation.The workshop is mostly lecture-based but will also provide some opportunities to practice the studied concepts using applied data examples in R.
Organizational structure of the course
The course will be delivered via Zoom, but all instructions will be live (no pre-recordings). The majority of the course will be lecture-based, but we will have some opportunities to practice what we learned on applied data examples. During the practical applications, participants will be given (simulated or real) data to work with in R. During these exercises, the instructor and a teaching assistant will be available to assist.

Target group

Researchers in the social sciences at all career stages who want to learn more about causal mediation analysis.

Learning objectives

By the end of the course, participants will:
  • understand the definition of the causal mediation effects that have been proposed in the literature,
  • be able to identify the underlying assumptions needed for each analysis,
  • be able to perform these analyses in R, and
  • finally, be able to confidently interpret the results of said analyses.

  • Prerequisites

  • solid working knowledge of the R programming language, with some familiarity with the tidyverse programming approach,
  • Solid understanding of regression analysis,
  • Some understanding of causal effects definitions using potential outcomes and DAGs.
    Software requirements
    We will be exclusively using the R programming language. It is advisable that participants have R and RStudio (or VSCode with R add-ins) already installed. Additionally, the tidyverse suite of packages and the “mediation” package are required but can be installed during the course.


    Recommended readings