Machine Learning

1 readers

1 users here now

Community Rules:

Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.

founded 11 months ago

MODERATORS

communick@academy.garden

[D] What are people working on when they say they work on Causal ML? (alien.top)

submitted 10 months ago by poitrenaud@alien.top to c/machinelearning@academy.garden

26 comments fedilink hide all child comments

Genuinely trying to understand.

you are viewing a single comment's thread
view the rest of the comments

[–] AlexiaJM@alien.top 1 points 10 months ago

Causality is linked to disentanglement and sparse solutions. If you assume that there is a true causal representation, then there are ways to provably recover any permutation of such a representation given some assumptions. And being causal, the representation will also naturally be disentangled and sparse (a cat will clearly be separated from a dog).

See https://proceedings.mlr.press/v202/lachapelle23a/lachapelle23a.pdf.