Can we just "pre-test" the backdoor criterion? I am trying to use DAGs to think more carefully about my regression models. I have a question about the "backdoor criterion", as usually seen in the DAG below (we are interested in the effect of $X$ on $Y$). One way to deal with confounding in this case is to include $Z$ in the regression, i.e.~control for it.
But if $Z$ is something that we can measure - for concreteness, in an economic context, $Z$ might be GDP, which often shows up as potential confounder in my DAGs - is it legitimate to first regress $X$ on $Z$ in order to verify that the hypothesised link $Z \to X$ actually exists? If it does, then we go ahead and include $Z$ in the model; if not, the DAG was incorrectly specified, and $Z$ is not in fact a confounder and does not need to be included.
Is this approach reasonable? What am I missing?


Yes, in order to confirm a confounding relationship you may perform a regression (or Chi-squared test or other suitable model) of $Z$ on $X$ and $Z$ on $Y$. This is exactly what I'm doing right now for a difference-in-differences healthcare analysis.
There may be confounders or colliders, measured or unmeasured, associated with $Z$ and $X$ or $Z$ and $Y$ that can cloud the true causal relationship between the variables. Therefore, it's important to flesh out the DAG prior to performing regression tests.
