Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory

DOI10.1142/S1230161210000126zbMath1190.62003arXiv0910.1691MaRDI QIDQ3573102

Bastian Steudel, Dominik Janzing

Publication date: 30 June 2010

Published in: Open Systems & Information Dynamics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0910.1691

Mathematics Subject Classification ID

Foundations and philosophical topics in statistics (62A01) Applications of statistics (62P99) Statistical aspects of information-theoretic topics (62B10)

Related Items (2)

Information-geometric approach to inferring causal directions ⋮ Least-squares independence regression for non-linear causal inference under non-Gaussian noise

Cites Work

• A Theory of Program Size Formally Identical to Information Theory
• Information geometry on hierarchy of probability distributions
• Algorithmic statistics
• Estimation of Entropy and Mutual Information
• On the Length of Programs for Computing Finite Binary Sequences
• A formal theory of inductive inference. Part II

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