It’s important to note this is causal inference from Pearl’s darling theory using do-calculus and graphical models.
I work in causal inference professionally (large scale causal impact measurement for observational studies that customers require causal interpretations of, for clinical trial data and advertising data mostly).
In practice, the stuff from Pearl is just unhelpful, and it’s very valuable to also read sources from Rubin, Imbens, Gelman and many others, because there are whole other approaches to the problem, approaches which more or less completely sidestep and have no need for do-calculus concepts, but which are extremely successful in practice, with real observational data sets.
Methods like propensity matching, propensity weighting, and hierarchical models using different treatment levels as strata, are absolutely critical for applying this stuff in the real world.
If you only ever read Pearl, you’ll mistakenly think those methods are somehow “inferior to” or “subsumed by” do-calculus when this is not at all true, certainly not in practice.
I work in causal inference professionally (large scale causal impact measurement for observational studies that customers require causal interpretations of, for clinical trial data and advertising data mostly).
In practice, the stuff from Pearl is just unhelpful, and it’s very valuable to also read sources from Rubin, Imbens, Gelman and many others, because there are whole other approaches to the problem, approaches which more or less completely sidestep and have no need for do-calculus concepts, but which are extremely successful in practice, with real observational data sets.
Methods like propensity matching, propensity weighting, and hierarchical models using different treatment levels as strata, are absolutely critical for applying this stuff in the real world.
If you only ever read Pearl, you’ll mistakenly think those methods are somehow “inferior to” or “subsumed by” do-calculus when this is not at all true, certainly not in practice.