Thanks for the update. Glad to know you are happy with your model. I would be careful with sweeping generalizations. What causal diagrams do is help us represent how we think our data were generated. This can be helpful for many data analyses applications both simple and complex, biomedical or other.
A simple illustrative example is our institutional kidney cancer data we analyze in figure 3 here. When we incorporated the plausible causal relationships in our simple regression models, they were able to reproduce the expected finding that kidney cancer subtype impacts overall survival.
Another tangible example is this datamethods thread which led to this publication.
Causal diagrams can also help showcase open problems in biostatistical methodology as discussed in this datamethods thread motivated by this commentary on adjuvant therapy considerations in oncolology.