[mlthoughts2018]

It's very important to note that the term 'causal inference' in this research paper is not the same thing as Pearl's causal inference techniques, and in fact the main two statistics and econometrics researchers cited in your linked article are Imbens and Rubin, two of the biggest critics of Pearl's methods.

The linked paper mostly goes into instrumental variables and mixed effects modeling for how classical econometrics has dealt with trying to understand the causality of intentionally varying a treatment. And, despite citing Rubin heavily, the paper doesn't go much into the Bayesian methods for solving similar problems (hierarchical models), even though they are a state of the art approach with modern computational MCMC techniques.

The last few sections do offer some interesting research citations for how classical instrumental effects models have been morphed with advances in machine learning, with things like causal trees.

But just look at one of the take away points of the survey, in section 5:

> "4. No fundamental changes to theory of identification of causal effects"

Overall, the link you've shared would be strongly in favor of ML-extended classical econometrics and possibly Bayesian hierarchical models or latent variable approaches, but almost surely would be against the notion that do-calculus could lead to a wide-spread or real-world set of applicable models.