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    $\begingroup$ "he basically fed his data into a few different machine learning frameworks and then [...] was able to pick out predictors and levels of significance." Leaving aside the problematic phrase "levels of significance", you will not obtain inferences of statistical significance without distributional assumptions. At that point, you might as well go back to classic statistical models since the change of variables for most ML models [...] $\endgroup$ Commented Aug 16, 2023 at 15:34
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    $\begingroup$ [...] becomes infeasible to work out. And since we do not have IID assumptions, you cannot just bootstrap a p-value from retraining your ML model on resamplings of the data. Furthermore, ML models often have parameters which cannot be readily coupled to human-understandable hypothesis. $\endgroup$ Commented Aug 16, 2023 at 15:34
  • $\begingroup$ I think the key is that ML models are not totally obscure. The value was the ability to pick out, from the mess of data that is a transplant patient's record, what was important and non-random. Again, it was about 12 years ago when he presented his work, so I am hoping that someone who works w/ various ML models--again, generalizations about "often" but rather looking for a particular ML--I want to say he was using a Bayesian network with kernel density estimation, but, as I mentioned, I was hoping to prompt someone who might be able to comment on the specifics, rather than just "most". $\endgroup$ Commented Aug 16, 2023 at 15:49
  • $\begingroup$ That doesn't have anything to do with the underlying problem when dealing with a small amount of information which is not independent and I don't have quite the statistical intuition to make any sort of recommendation without knowing what the data actually looks like. $\endgroup$ Commented Aug 16, 2023 at 15:53
  • $\begingroup$ A Bayesian network is explicitly a probability distribution, representing a factorization of a joint distribution using the chain rule of probability, which relates to what I was saying about distributional assumptions being required to make inferences of statistical significance. Used in conjunction with KDE this makes the inference a nonparametric statistical procedure. There are no specifics to comment on unless they are provided, and without them there are too many possibilities to consider or discuss. $\endgroup$ Commented Aug 16, 2023 at 16:06