For those interested in Causal Inference, here is Judea Pearl's fairly accessible intro "An Introduction to Causal Inference":
http://ftp.cs.ucla.edu/pub/stat_ser/r354-corrected-reprint.p...
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.
In some cases, you would need the framework of do-calculus, but very often when other assumptions are reasonable, you don’t.
When using matching, regression adjustment, propensity techniques, or hierarchical models that separate strata based on the treatment to measure causal effects of treatments, and you believe the underlying assumptions are generally satisfied, you have utterly no need for anything do-calculus related, not even theoretically, and certainly not in terms of practically actually fitting models to the data.
Have you got some readings? I've been reading the Pearl books on this subject.
Another good reference is [1]. Rubin invented a lot of observational data methods for correcting to measure causal effect. Imbens is also a prolific author in this area, and even just googling for propensity model papers from Imbens will leads to many methods and many other papers.
[0]: < http://www.stat.columbia.edu/~gelman/arm/ >
[1]: < https://www.amazon.com/Causal-Inference-Statistics-Biomedica... >
I'm going to echo my sibling commenter, would love to hear it if you have any pointers on where to start reading.
I'm a software engineer but data work comes up all the damn time. Seeing coworkers approach causality in a hand-wavy way without knowing a more rigorous approach to suggest is frustrating! I had hoped Pearl's book was exactly that.