yeah, i've wondered about this too. the natural thing to do is to just unroll the models in time. the classic case of this is a hidden Markov Model, but you ca

yeah, i've wondered about this too. the natural thing to do is to just unroll the models in time. the classic case of this is a hidden Markov Model, but you can easily have a more complicated time-indexed DAG. There are also some interesting connections to reinforcement learning (MDP can be expressed as DAG e.g. https://www.microsoft.com/en-us/research/wp-content/uploads/2013/11/bottou13a.pdf https://www.microsoft.com/en-us/research/wp-content/uploads/..., and then many RL algorithms are equivalent to causal inference estimators). it seems like people deal with equilibria and feedback loops in a pretty ad-hoc way though.