Feb. 15, 2024, 5:43 a.m. | Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar

cs.LG updates on arXiv.org arxiv.org

arXiv:2303.02186v2 Announce Type: replace
Abstract: Causality has the potential to truly transform the way we solve a large number of real-world problems. Yet, so far, its potential largely remains to be unlocked as causality often requires crucial assumptions which cannot be tested in practice. To address this challenge, we propose a new way of thinking about causality -- we call this causal deep learning. Our causal deep learning framework spans three dimensions: (1) a structural dimension, which incorporates partial yet …

abstract arxiv assumptions causality challenge cs.ai cs.lg deep learning practice solve thinking type unlocked world

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