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3548-nonlinear-causal-discovery-with-additive-noise-models.pdf
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🔗 Source: papers.nips.cc
📊 Size: 454 KB
👤 Author: Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf
⬇️ Downloads: 670

Summary

Nonlinear causal discovery with additive noise models is a method for identifying causal relationships between continuous-valued variables. Traditional linear acyclic causal models with additive noise are often used, but many causal relationships are nonlinear. This contribution shows that nonlinearities in the data-generating process can provide information on the underlying causal system, allowing for more aspects of the true data-generating mechanisms to be identified. Theoretical results and simulations demonstrate the identification power provided by nonlinearities, and a practical method for inferring the generating model from a sample of data vectors is described. The method is based on the idea that nonlinearities can break the symmetry between observed variables, allowing for the identification of causal directions. The approach is illustrated with examples and experiments, including a two-variable model and a more general case with N variables.

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Nonlinear causal discovery with additive noise models is a method for identifying causal relationships between continuous-valued variables.

Technical Information

  • File Format: PDF
  • File Size: 454 KB
  • Pages: 8
  • Language: EN
  • Author: Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf
  • Total Downloads: 670
  • Last Updated: 3 weeks ago

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