4 ms·

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_s

• mlthoughts2018 8y ago
  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.
  • zandl 8y ago
    My understanding is that the do is needed for counterfactual reasoning, so if I were to do x assuming condition y. And that is lacking in other approaches? Or am I mistaken?
    • mlthoughts2018 8y ago
      You definitely don’t need do for counterfactual reasoning, that is a big misconception. The misconception is usually rooted in a misunderstabding of what “doing causal inference” means, and what specific assumptions are being made, e.g. exclusion restriction, SUTVA, exogeniety in a propensity model, balance between treated and non-treated groups. 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.
      • zandl 8y ago
        The cases where you do need it are in creating a generalized AI, perhaps your use case is only for epidemiology, and of course that’s Perl’s background too. But his arguing for general purpose AI and using his epidemiological expirence for demonstrating since it’s what he knows well. That’s my understanding from The Book of Why at least.
  • lordnacho 8y ago
    > 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. Have you got some readings? I've been reading the Pearl books on this subject.
    • mlthoughts2018 8y ago
      A good starting point is chapters 9 & 10 from [0]. Then many of the same topics are re-discussed in the second half of the book through the lens of Bayesian hierarchical models. 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/ http://www.stat.columbia.edu/~gelman/arm/ > [1]: < https://www.amazon.com/Causal-Inference-Statistics-Biomedical-Sciences/dp/0521885884 https://www.amazon.com/Causal-Inference-Statistics-Biomedica... >
  • mycelium 8y ago
    there are whole other approaches to the problem... which are extremely successful in practice, with real observational data sets. 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.
    • mlthoughts2018 8y ago
      See my response to the other comment, < https://news.ycombinator.com/item?id=18002046 https://news.ycombinator.com/item?id=18002046 >.