> I'm surprised to see it viewed so negatively here, dismissed with no engagement with his specific arguments and examples.
I struggle to motivate engaging with it because it is unfortunately quite out of touch with (or just ignores) some core issues and the major advances in causal modeling and causal modeling theory, i.e. Judea Pearl and do-calculus, structural equation modeling, counterfactuals, etc [1].
It also, IMO, makes a (highly idiosyncratic) distinction between "statistical" (meaning, trained / fitted to data) and "probabilistic" models, that doesn't really hold up too well.
I.e. probabilistic models in quantum physics are "fit" too, in that the values of fundamental constants are determined by experimental data, but these "statistical" models are clearly causal models regardless. Even most quantum physical models can be argued to be causal, just the causality is probabilistic rather than absolute (i.e. A ==> B is fuzzy implication rather than absolute implication). It's only if you ask deliberately broad ontological questions (e.g. "Does the wave function cause X") that you actually run into the problem of quantum models being causal or not, but for most quantum physical experiments and phenomena generally, the models are still definitely causal at the level of the particles / waves / fields involved.
IMO I don't want to engage much with the arguments because it starts on the wrong foot and begins by making, in my opinion, an incoherent / unsound distinction, while also ignoring or just being out of date with the actual scientific and philosophical progress and issues already made here.
I would also say there is a whole literature on tradeoffs between explanation (descriptive models in the worst case, causal models in the best case) and prediction (models that accurately reproduce some phenomenon, regardless of if they are based on and true description or causal model). There are also loads of examples of things that are perfectly deterministic and modeled by perfect "causal" models but which are of course still defy human comprehension / intuition, in that the equations need to be run on computers for us to make sense of them (differential equation models, chaotic systems, etc). Or just more practically, we can learn to do all sorts of physical and mental skills, but of course we understand barely anything about the brain and how it works and co-ordinates with the body. But obviously such an understanding is mostly irrelevant for learning how to operate effectively in the world.
I.e. in practice, if the phenomenon is sufficiently complex, an accurate causal model that also accurately models the system is likely to be too complex for us to "understand" anyway (or you just have identifiability issues so you can't decide between multiple different models; or you don't have the time / resources / measurement capacity to do all the experiments needed to solve the identifiability problem anyway), so there is almost always a tradeoff between accuracy/understanding. Understanding is a nice luxury, but in many cases not important, and in complex cases, probably not achievable at all. If you are coming from this perspective, the whole "quandary" of the essay seems just odd.
[1] https://plato.stanford.edu/entries/causal-models/
