What is a causal model and how is it different from a "common" statistical model?
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Thread on a mental picture and intuition how one may think about (a subclass of) causal models and the causal discovery problem.
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@bttyeo @eliasbareinboim @KordingLab @EpiEllie @causalinf A "common" statistical model models one joint distribution over variables X = {A, B, …}; a causal model models a set of joint distributions over X, one for each intervention.
Here line segments correspond to the modellable distributions for varying model parameters.
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Does the number of boxes loaded _cause_ the risk of a truck rolling over?

In what ways does confounding (or having access only to certain macro-variables) limit causal inference in neuroimaging?

How are these two questions related?

Check out the thread below 👇

1/ Bear with me, in an attempt to strip down the problem and to provide a starting point for a constructive discourse I am deliberately not using neuro lingo to begin with.
I hope the following idealised simplified toy example turns out to be instructive.

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