The article is a little dense for me, and presumes knowledge about Probabilistic Graphical Models (PGMs) and directed acyclic graphs / causal Bayesian networks (DAGs) without introducing the background knowledge - so, I expect my reading of it missed a lot of the details.
But the one thing I came out wondering, is if you do a proper randomized double-blinded study with a large population sample, say, taking 1000 people with a particular illness, completely randomizing them, and then giving 500 of the sample a particular treatment, and 500 of the sample a placebo that is indistinguishable from the treatment. If an unbiased third-party, then observes the groups (still without knowledge of which group had which treatment), and if one group shows markedly different results (say, 490/500 of the treatment group recover, and only 10/500 placebo group recover), is it fair to say that in this particular scenario, that correlation of recovery with the treatment implied that the treatment caused the recovery?
But the one thing I came out wondering, is if you do a proper randomized double-blinded study with a large population sample, say, taking 1000 people with a particular illness, completely randomizing them, and then giving 500 of the sample a particular treatment, and 500 of the sample a placebo that is indistinguishable from the treatment. If an unbiased third-party, then observes the groups (still without knowledge of which group had which treatment), and if one group shows markedly different results (say, 490/500 of the treatment group recover, and only 10/500 placebo group recover), is it fair to say that in this particular scenario, that correlation of recovery with the treatment implied that the treatment caused the recovery?