Check out MuZero. It learns an embedding of the game's state space potentially allowing AlphaGo like dominance in more ways.
(Actually, ML is still not very good at causal reasoning so we have sometime. I'm more excited and worried about crispr at this point; what happens when we can make people genetically super human)
> no AI that I know of has at its disposal a full blown model of the world it operates in
This is a field called Model-based Reinforcement learning, and it's quite advanced already -- there are indeed models that have an internal state reflecting the world state.
A good recent example:
> deep learning model, however much we'd like to think they do, aren't capable of doing proper causal inference in a general setting
This is also addressed by recent models, somewhat. Once you have an abstract world model, searching for a high reward can be just a matter of running markovian simulation on it using high reward heuristics (given by a network of course), like AG does. This line is also very active right now, one example is the recent MuZero.
Inference at its core really isn't much more than an artful curve fitting (or an artful model search if you like), and it's one of the building blocks of intelligence.