Relationship between do intervention versus uplifting models based with causal graph aware estimand

[tonyabracadabra]

I've been browsing through the codebases of several popular causal inference libraries and I am especially interested in the relationship of do-intervension vs uplifting models (methods in econml/causalml).

So here is my understanding: It feels like do-intervention does a pretty similar thing as compared to doWhy's identify-estimate step, where the estimation of an action is evaluated on an estimand retrieved from a causal graph with do calculus, but the only difference is causalnex is using bayesian network for inference instead of models commonly used in CATE.

Am I thinking it in a correct way or did I miss anything? If they are different, what are their particular use cases and what are the caveats I should be aware of when choosing these methods?