Pearl-style SCMs assume that every single node in a graph is ontologically independent, which makes unrolled models as suggested not particularly great.
From a paper co-authored by Pearl himself:
The problem with using structural causal models is that the language of structural models is simply not expressive enough to capture certain intricate relationships that are important in causal reasoning. The ontological commitment of these models is to facts alone, assignments of values to random variables, much like propositional logic. Just as propositional logic is not a particularly effective tool for reasoning about dynamic situations, it is similarly difficult to express dynamic situations (with objects, relationships, and time) in terms of structural causal models.
I haven’t been active in causality research since about 5 years ago, but I’m not aware of any good solutions to the time problem. I do know there are proposals for models that make improvements for causality involving sets of related variables, e.g.: platelet models. I think our own work on counterfactual probabilistic programming has a pretty strong basis, although the philosophy is fairly abridged in the paper.
Pearl-style SCMs assume that every single node in a graph is ontologically independent, which makes unrolled models as suggested not particularly great.
From a paper co-authored by Pearl himself:
( https://commonsensereasoning.org/2005/hopkins.pdf )
I haven’t been active in causality research since about 5 years ago, but I’m not aware of any good solutions to the time problem. I do know there are proposals for models that make improvements for causality involving sets of related variables, e.g.: platelet models. I think our own work on counterfactual probabilistic programming has a pretty strong basis, although the philosophy is fairly abridged in the paper.