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Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective

  • Kun Zhang
  • Aapo Hyvärinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5782)

Abstract

We consider causally sufficient acyclic causal models in which the relationship among the variables is nonlinear while disturbances have linear effects, and show that three principles, namely, the causal Markov condition (together with the independence between each disturbance and the corresponding parents), minimum disturbance entropy, and mutual independence of the disturbances, are equivalent. This motivates new and more efficient methods for some causal discovery problems. In particular, we propose to use multichannel blind deconvolution, an extension of independent component analysis, to do Granger causality analysis with instantaneous effects. This approach gives more accurate estimates of the parameters and can easily incorporate sparsity constraints. For additive disturbance-based nonlinear causal discovery, we first make use of the conditional independence relationships to obtain the equivalence class; undetermined causal directions are then found by nonlinear regression and pairwise independence tests. This avoids the brute-force search and greatly reduces the computational load.

Keywords

Mean Square Error Causal Relation Directed Acyclic Graph Granger Causality Conditional Independence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kun Zhang
    • 1
  • Aapo Hyvärinen
    • 1
    • 2
  1. 1.Dept of Computer Science & HIITUniversity of HelsinkiFinland
  2. 2.Dept of Mathematics and StatisticsUniversity of HelsinkiFinland

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