Re:ML Beyond Curve Fitting: An Intro to Causal Inference and...
by 0101010110 on Tuesday June 22, 2021 @03:31PM
(#27596081)
The rules also provide a framework within which you can rule out some causal relationships. So they at least go some way to confirming which hypotheses can't be correct given the data.
You basically need to make some assumptions that are broadly equivalent to assuming you've already correctly guessed certain parts of the underlying causal structure. So in a certain sense you're kind of begging the question, in a way that you wouldn't need to do if you had the ability to do interventions/randomized trials.
That being said causal inference techniques are still very valuable in making explicit exactly what assumptions you're making and how those affect your final conclusion and therefore how to minimize the impact of those assumptions.