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CAUSALITY FOR MACHINE LEARNING

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Wouter van Amsterdam: Bernhard Scholkopf (@bschoelkopf) just published a single author paper titled "Causality for Machine Learning" (https://arxiv.org/abs/1911.10500); this should probably at the top of the reading list for many people interested in machine learning / AI; @yudapearl @eliasbareinboim

1 replies, 744 likes


Judea Pearl: A very comprehensive, delightful and inspiring paper. Recommended to ALL, not just MANY ML/AI folks. Note also that @bschoelkopf does not perceive me as "polarizing the field" as suggested by @ylecun here: https://twitter.com/ylecun/status/1198319448320548865. SCM unifies, invites and educates. #Bookofwhy

6 replies, 348 likes


Eric Topol: A big problem with #AI is that it hasn't read and couldn't understand @yudapearl's Book of Why. This easy to understand essay by @bschoelkopf (and inspired by Pearl) takes us through the gaps in ML thinking and reasoning, cause and effect https://arxiv.org/abs/1911.10500 @MPI_IS https://t.co/hSQg3PsNKN

5 replies, 191 likes


Anirudh Goyal: This is probably the most succinct summary https://arxiv.org/abs/1911.10500 of various ways in which causality could be useful for machine learning by @bschoelkopf Highly recommended.

1 replies, 126 likes


ML Review: Causality for Machine Learning By @bschoelkopf Mostly non-technical intro to key causal models and how they can contribute to resolving open ML problems like generalization across domains or "thinking" (i.e., acting in an imagined space) https://arxiv.org/abs/1911.10500 https://t.co/gEyuWTKLbi

0 replies, 113 likes


Judea Pearl: There's much truth to what you're saying. The idea that there are theoretical impediments to ML methods is hard for ML folks to internalize.And repeated assurances that causal inference is just one aspect of what ML has been doing all along do not encourage them to try.#Bookofwhy

1 replies, 82 likes


Kyle Cranmer: Bernhard Scholkopf (@bschoelkopf) will be speaking at our #NeurIPS2019 workshop on Machine Learning for Physical Sciences https://ml4physicalsciences.github.io

0 replies, 26 likes


KordingLab: Scholkopf on how Pearl style causality is starting to touch machine learning. H/T @danilobzdok

0 replies, 21 likes


Kostas Kamnitsas: Causality for Machine Learning. https://arxiv.org/abs/1911.10500 Tubingen at it again. (Keeps surprising me how such a small place gave rise to such a great research group. I keep wondering what's the recipe. @MPI_IS )

0 replies, 13 likes


Geir Kjetil Sandve: I still find that learning things trumps doing things any day. Thanks to @bschoelkopf and @eliasbareinboim for giving me two distinct, fantastic reading experiences on papers I just recently discovered - https://arxiv.org/abs/1911.10500 and https://www.pnas.org/content/113/27/7345

0 replies, 6 likes


Amit Sharma: Appropos Bengio's talk at #neurips19, here's a great summary on applying causal reasoning for machine learning problems like generalization and adversarial robustness https://arxiv.org/abs/1911.10500 by @bschoelkopf #causalML

0 replies, 4 likes


Italian Association for Machine Learning: Great review on causality in machine learning by @bschoelkopf : https://arxiv.org/abs/1911.10500 https://t.co/HT71oZFGoS

0 replies, 3 likes


e-Katerina Vylomova: @zacharylipton Also, enjoyed reading Schölkopf's recent paper on Causality for ML: https://arxiv.org/pdf/1911.10500.pdf (and "The Book of Why" by Judea Pearl and Dana MacKenzie)

0 replies, 3 likes


Seyed Mostafa Kia: #CAUSALITY FOR #MACHINE_LEARNING by @bschoelkopf "the hard open problems of machine learning and AI are intrinsically related to causality" https://arxiv.org/pdf/1911.10500.pdf

0 replies, 1 likes


Aaron Snoswell (薛嘉伦): Today's #MachineLearning paper is @bschoelkopf's recent philosophical essay 'Causality for Machine Learning' (https://arxiv.org/pdf/1911.10500.pdf) Given an unlabelled scatter plot in arbitrary units, can you tell if X causes Y? Y causes X? There is a common unobserved causal factor? 1/3 https://t.co/qMrDLZRTZ9

1 replies, 1 likes


Juan Arévalo: A must read. The connection between #causality and #MachineLearning by @bschoelkopf https://arxiv.org/abs/1911.10500

0 replies, 1 likes


Danilo Bzdok: @KordingLab @TheColeLab @sweichwald @f2harrell

0 replies, 1 likes


Eric Topol: @pradeu Agree, Thomas. Casuality can't be emphasized enough. @yudapearl's book is my favorite source. And it's now coming up with #AI (that it can't) https://twitter.com/EricTopol/status/1199446511823679489

0 replies, 1 likes


Paul S. Conyngham: Causality for Machine learning! read it at 👇 https://arxiv.org/pdf/1911.10500.pdf

0 replies, 1 likes


Pawan Sasanka Ammanamanchi: https://arxiv.org/abs/1911.10500 Causality in Machine Learning.

0 replies, 1 likes


Nasim: A primer on the emerging connections between Causal Inference and Machine Learning, by @bschoelkopf: https://arxiv.org/abs/1911.10500

0 replies, 1 likes


Thread Reader App: @IntuitMachine Guten tag, you can read it here: Thread by @IntuitMachine: High time to read: "Causality for Machine Learning" http://arxiv.org/abs/1911.10500… by Bernhard… https://threadreaderapp.com/thread/1200051422018113542.html. See you soon. 🤖

0 replies, 1 likes


Complex Human: Causality for Machine Learning https://arxiv.org/abs/1911.10500

0 replies, 1 likes


Content

Found on Nov 26 2019 at https://arxiv.org/pdf/1911.10500.pdf

PDF content of a computer science paper: CAUSALITY FOR MACHINE LEARNING