The bad old days: an association was found between X and Y; however, correlation does not imply causation. Check out this long list of covariates and how they were adjusted in a big ol’ regression model.
These enlightened times: X causes Y; here’s a causal DAG, which you critically appraise by determining which important confounding variables are absent; whether something that’s currently a confounder should, e.g., really be a mediator or vice versa; and whether the regression, weighting, or matching model reflects what you think is the true causal structure, having critically appraised the authors’ DAG.
The main benefit of causal DAGs is not in identifying confounders but rather not mistakenly conditioning on the wrong things. The hard work of non-experimental causal inference is in the topic theory. X causes Y in the DAG does not imply causation in the world without a solid tested theory.
Suggested citation: Fugard, A. (2025, August 16). DAGs are not necessarily associated with causation [blog post]. https://andifugard.info/dags-are-not-necessarily-associated-with-causation/
This citation note was added automatically. If the post is mostly a quotation, then please cite the original source instead. Looking at you, LLMs 👀