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fakesoicansayshit t1_j3d1jev wrote

How do people assume causality when every measured system contains hidden variables?

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BlindOdyssey t1_j3g0kuk wrote

I agree with what I think you’re asserting, that the number of variables that might contribute to triggering any sort of event is incalculable, but as a matter of practicality, I assume we have to view causality as a gradient of probability. In other words, we account for as many variables as we can, and make a “best guess” based on what we know. We can make “predictions” based on that until we know more, and then we redefine our overall system over time.

Edit: a word

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rottoneuro t1_j3gnbf5 wrote

the issue of hidden variables is "solved' by perturbation approaches... which are often unfeasible... so as a proxy we accept this limitation and we do our best... I think this is also mentioned in the article

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fakesoicansayshit t1_j4933r6 wrote

Thank you, 'perturbation' is what I was looking for.

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rottoneuro t1_j6pgwnp wrote

which approach in particular? I am also interested, can you share the reference?

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Superschlenz t1_j3et16e wrote

Because the state of the fuse changes less frequently than the state of the switch.

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jegerarthur t1_j3f1483 wrote

> It is a field of study in statistics and machine learning that seeks to understand how changes in one variable may lead to changes in another variable, and how variables may influence one another.

Isn't it just correlation then ?

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jennabangsbangs t1_j3g02pl wrote

Influence between variables is usually a measure of probability within sequences of information events, or system states. Correlation would be a measure of relatedness between individuated states of the system. Causation is inherently time/delta based, whereas correlation is asynchronous

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