I don’t know too much about this space, but Uber’s Causal ML python library & its uses may be a good place to look. That or Pyro, also made by Uber. Presumably Uber’s uses for these tools are success cases, but I don’t know the details. John has talked about Pyro being cool in previous posts of his, so he could have in mind the tools it provides when he talks about this.
Looking superficially, neither really seems to be doing causal structure discovery. Causal ML roughly speaking seems to be doing various form of multivariate regression, whereas Pyro seems to be fitting Bayesian networks. Both of these goals require assumptions from causal structure discovery, but they are not in themselves examples of causal structure discovery.
I don’t know too much about this space, but Uber’s Causal ML python library & its uses may be a good place to look. That or Pyro, also made by Uber. Presumably Uber’s uses for these tools are success cases, but I don’t know the details. John has talked about Pyro being cool in previous posts of his, so he could have in mind the tools it provides when he talks about this.
Looking superficially, neither really seems to be doing causal structure discovery. Causal ML roughly speaking seems to be doing various form of multivariate regression, whereas Pyro seems to be fitting Bayesian networks. Both of these goals require assumptions from causal structure discovery, but they are not in themselves examples of causal structure discovery.