Studies // Cited work
Thinking clearly about correlations and causation: Graphical causal models for observational data
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data.
Level D Review doi:10.1177/2515245917745629
What this study supports
Supports statements that correlation does not establish causation, that statistical control depends on assumptions, and that controlling for colliders or mediators can bias estimates. Methods guidance.
- Who was studied
- Not applicable: a tutorial on graphical causal models for observational data in psychology.
- Design
- Review
- Level of evidence
- Cross-sectional, observational, qualitative work and narrative reviews.
Claims that rest on it
No claim in the ledger rests on this work yet.