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Group invariance principles for causal generative models

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Besserve,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Shajarisales,  N
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Janzing,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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 (pp. 557-565). Madison, WI, USA: International Machine Learning Society.


Cite as: https://hdl.handle.net/21.11116/0000-0001-7D56-3
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.