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  Group invariance principles for causal generative models

Besserve, M., Shajarisales, N., Schölkopf, B., & Janzing, D. (2018). Group invariance principles for causal generative models. In A. Storkey, & F. Perez-Cruz (Eds.), International Conference on Artificial Intelligence and Statistics, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands (pp. 557-565). Madison, WI, USA: International Machine Learning Society.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0001-7D56-3 Version Permalink: https://hdl.handle.net/21.11116/0000-0001-ACD2-0
Genre: Conference Paper

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 Creators:
Besserve, M1, 2, Author           
Shajarisales, N, Author
Schölkopf, B, Author           
Janzing, D, Author           
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

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 Abstract: The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms. The interpretation of independence and the way it is utilized, however, varies across these methods. Our aim in this paper is to propose a group theoretic framework for ICM to unify and generalize these approaches. In our setting, the cause-mechanism relationship is assessed by perturbing it with random group transformations. We show that the group theoretic view encompasses previous ICM approaches and provides a very general tool to study the structure of data generating mechanisms with direct applications to machine learning.

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 Dates: 2018-04
 Publication Status: Issued
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 Identifiers: BibTex Citekey: BesserveSSJ2018
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Title: 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018)
Place of Event: Playa Blanca, Spain
Start-/End Date: 2018-04-09 - 2018-04-11

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Title: International Conference on Artificial Intelligence and Statistics, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands
Source Genre: Proceedings
 Creator(s):
Storkey , A, Editor
Perez-Cruz, F, Editor
Affiliations:
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Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 557 - 565 Identifier: -

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Title: PMLR Proceedings of Machine Learning Research
Source Genre: Series
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Pages: - Volume / Issue: 84 Sequence Number: - Start / End Page: - Identifier: -