Introduction to causal inference


  • Introduction to causal inference

Learning objectives

At the end of the session, students should be capable of

  • understanding the importance of listing potential confounders
  • determining which variables to control for (confounders vs colliders)
  • drawing a directed acyclic graph describing the interrelation between variables
  • explaining the differences between experimental and observational studies for studying mediation


  • Chapter 2 of VanderWeele (2015)

Andrew Heiss’ course notes on directed acyclic graphs (DAG) and types of association.

The structural equation modelling (SEM) approach to mediation

The causal inference approach

Complementary readings

  • Pearl et al. (2016), Chapter 3
  • Rohrer (2018)


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Baron, R., & Kenny, D. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
Bullock, J. G., Green, D. P., & Ha, S. E. (2010). Yes, but what’s the mechanism? (Don’t expect an easy answer). Journal of Personality and Social Psychology, 98(4), 550–558.
Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15(4), 309–334.
Pearl, J. (2014). Interpretation and identification of causal mediation. Psychological Methods, 19(4), 459–481.
Pearl, J., Glymour, M., & Jewell, N. (2016). Causal inference in statistics: A primer. Wiley.
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42.
VanderWeele, T. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.