4 ms·

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 a

• 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.