Why machine learning struggles with causality

Re:Why machine learning struggles with causality by musingsole on Friday April 02, 2021 @02:03PM (#26674011)

Empirical evidence is a special case observation. If you observe the whole universe in its entirety, you could separate moments that effectively followed whatever conditions you might set in a lab. You can't act on the system, but you can forever tune your models to match the infinite observations you could record. At that point the separation between observation derived knowledge versus experiential knowledge is meaningless (it's just hard to imagine a universal model manifesting without having used experiental knowledge along the way).

Whether you swing the bat or just watch it hit the ball into the sky, you have the prerequisites needed to reason about the interaction.

A more entertaining question is how a system comes to believe causality (i.e. comes to believe that things can and must have causes)

[ Parent | on HN ]

Re:Why machine learning struggles with causality by jozvolskyef on Friday April 02, 2021 @07:58PM (#26677135)

I agree. To say we're able to influence the system is to assume free will, which I believe is an undecidable problem. Ultimately this is just a matter of semantics, which makes this thread rather pointless.

[ Parent | on HN ]

Re:Why machine learning struggles with causality by PeterisP on Saturday April 03, 2021 @05:46AM (#26679622)

It does not need to assume free will, which indeed is a much harder philosophical issue - it needs to assume that the decisions of the intervention are caused by factors outside of the system you're studying (which usually isn't the whole world), which is easy to get if you have some actual external influence, and very hard to determine if you're purely observing.

For example, if you're allocating patients in control and treatment group based on a roll of dice - the dice don't have free will, but they are influencing the "system" of the patients and their treatment, while that system is not causually influencing the dice rolls.

For another example, if a baby is "experimenting" by babbling (a key part of language acquisition, https://en.wikipedia.org/wiki/Babbling), it's not necessary or relevant to decide whether free will is involved, the baby obtains useful experimental results of what sound experiences are caused by which attempts to move the tongue/mouth/etc, even if there's no free will and the attempts to move the tongue/mouth/etc are also deterministically caused by the sensory experiences of the baby.

[ Parent | on HN ]

Re:Why machine learning struggles with causality by still_grokking on Friday April 02, 2021 @05:03PM (#26675975)

> (it's just hard to imagine a universal model manifesting without having used experiental knowledge along the way)

That's actually the point!

[ Parent | on HN ]