[closed]

In theory, yes. However, I think in practice addressing the concerns of critics is often out of Pearl's hands.

Until they supply a "ground truth" or data generating model, he has a dilemma:

* if he doesn't create a data generating model, then arguments for / against his approach will be specious.

* if he creates a data generating model, they can claim it doesn't reflect reality.

In the case of Judea Pearl and Andy Gelman, it seems like the point of contention is much broader than the do-calculus. Andy Gelman does not seem to be a fan of structural equation modeling / similar graphical models.

[mlthoughts2018]

How is it out of Pearl’s hands? Also, Gelman & Rubin already did look into Pearl’s models, and even agreed that for some toy model examples, the technique works as intended, but that there are serious how-things-work-in-practice reasons why Pearl’s models are unlikely to be mathematically appropriate for some real world use cases.

It’s really a fair response from them to Pearl, especially when the whole time Pearl is presenting it like causal inference is a miracle cure-all.

All I am seeing in your comments is hand waving attempts to shift the burden of proof onto the group of practitioners who already looked into this stuff and weren’t convinced!

So why does it being incumbent on Pearl or on another causal inference practitioner to demonstrate it scaling up to a more complicated in-practice problem still get qualified with an “in theory” from you? Why isn’t it resoundingly obvious by this point that the burden of proof lies with Pearl, and that people would be happy to hear if he can use these models for large-scale, practical use cases, but they (rightfully) don’t see a reason (even after looking into the models) to spend their own time doing it?