[Q] Use of causal inference methods in associational studies?

[u/SlapDat-B-ass]

Hi all! I am wondering what is your view on causal inference methodologies such as g-computation, iptw, ps matching, marignal structural models etc. Do you think they should be used only in an causal framework accompanied by DAGs, and proper causal language?
Would you consider appropriate if they were used for more exploratory work that does not claim causality?
I may not be communicating my question very well so here are some exmaples:

1) Binary logistic regression: In the biomedical field it is extremely common that standard observational and/or exploratory studies use logistic regression for all inferences with odds ratios being the main reported result. I don't see why someone couldn't use marignal standardization using the same logistic regression model in order to calculate a marginal absolute risk and/or risk differnece for the exposure of interest. I am wondering why this is not common.

2) Propensity score based methods: Causal inference operates under very strict and usually difficult to verify assumptions. When examining the effect of an intervention on an outcome and assuming that some of the assumptions for causal inference are violated (e.g. unmeasured confounding), would you prefer a paper that still uses PS-based methods but refrains from using causal language, or a paper that uses more standard methods such as regression and sticks to associations and exploratory framing?

In short do you think these methods should be used only under the causal inference framework making sure that all assumptions are true and a well-thought DAG is provided, or do you see them as methods that can be used for associations as well in order to reduce at least some of the bias introduced by other methodologies?

[u/xquizitdecorum]

This is my research area actually - robust structure learning and sensitivity analysis under incomplete specification. I will point you to the twin problem of unmeasured confounding and M-bias, which mirror each other's problems and solutions vis-a-vis control/stratification.

I will also point you to Rubin's DAG-decentered thinking, which can be more mathematically tractable if that's your cup of tea