Introduction to causal inference

Content

  • 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

Readings

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)

Slides

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Tip

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References

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. https://doi.org/10.1037/0022-3514.51.6.1173
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. https://doi.org/10.1037/a0018933
Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15(4), 309–334. https://doi.org/10.1037/a0020761
Pearl, J. (2014). Interpretation and identification of causal mediation. Psychological Methods, 19(4), 459–481. https://doi.org/10.1037/a0036434
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. https://doi.org/10.1177/2515245917745629
VanderWeele, T. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.