There are various notions of this.
The most basic/naive one is where one can estimate the unknown parameters of the model given example token streams generated by the model.
The most basic/naive one is where one can estimate the unknown parameters of the model given example token streams generated by the model.
Identifiability means that out of all possible models, you can learn the correct one given enough samples.causal identifiability has some other connotations
See here https://causalai.net/r80.pdf as a good start (a nose in a causal graph is Markov given its parents, and a k-step Markov chain is a k-layer causal dag)