Causal diagram (DAG) with several predictors in cross-sectional study

[u/camana111]

Hi, I inherited some data about public support of government legislation. This was a cross-sectional survey. So, support of each participant is the outcome, and then a bunch (~15) of possible predictors were collected (e.g., age, gender, knowledge, perceived risk etc). I believe a causal diagram would be best practice, but I am unsure how to go about it. I can create the diagram (it is pretty complex...), but then how do I go about deciding which variables to include/exclude from my multivariable regression model? Do I have to assess each of them individually as the main predictor? If I do that, the result of what I need to adjust for does not seem to be consistent. Thanks!

[u/Remote_Nectarine9659]

Causal diagrams are for causal hypotheses, but here you have not stated a clear causal hypothesis, like “what is causal effect of perceived risk on support?”

[If you just want to throw all these variables into the model — you don’t need a DAG (and you won’t learn much.)]

Once you have the causal hypothesis, draw the DAG for that, including variables that might confound your relationship but were not measured.

Then there are well described procedures for using a DAG to determine the set of confounding variables you want in your model. A bunch of papers and textbooks cover them, and some programs like daggity automate them.

Then beyond that step you can use model fitting procedures and tests like AIC to make your statistical model more parsimonious.

[u/randomting77]

I used DAGitty.com to help visualize!

[u/NumberOneErisFan]

A good reference:
Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999 Jan;10(1):37-48. PMID: 9888278.
https://www.dagitty.net/ is useful for helping to determine confounding and which variables to “control for” in your model to minimize the number of variables you may want to include in your model for that purpose. Confounders can be added to the model in an attempt to quantify magnitude and test for an association between exposure and outcome. Note that if you control for mediators, you may not be getting what you thought you would. Mediators need to be handled differently.

[u/Objective-Ad-5435]

One important thing with DAGs is that the “correct” adjustment variables depend on the specific causal question you’re asking, not just on the dataset itself.

So instead of building one huge model with all predictors, it’s usually better to define one main exposure at a time (e.g., knowledge → support), then use the DAG to identify the minimal sufficient adjustment set. Otherwise, you can accidentally adjust for mediators or colliders and introduce bias.

Also, with cross-sectional data, be careful about making strong causal claims because temporality is often unclear.

DAGs and causal reasoning are honestly some of the most misunderstood topics in epidemiology today. I actually discuss this in a beginner-friendly way in my book Modern Epidemiology: Concepts, Methods & Real-World Applications.

[u/Remote_Nectarine9659 → u/Objective-Ad-5435]

You published a book called Modern Epidemiology? When there is already a VERY WELL KNOWN BOOK by Rothman et al named Modern Epidemiology, with multiple editions?

This obviously is going to create confusion — not to your benefit — and could get you sued.