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Estimating a Causal Order among Groups of Variables in Linear Models

  • Doris Entner
  • Patrik O. Hoyer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

Abstract

The machine learning community has recently devoted much attention to the problem of inferring causal relationships from statistical data. Most of this work has focused on uncovering connections among scalar random variables. We generalize existing methods to apply to collections of multi-dimensional random vectors, focusing on techniques applicable to linear models. The performance of the resulting algorithms is evaluated and compared in simulations, which show that our methods can, in many cases, provide useful information on causal relationships even for relatively small sample sizes.

Keywords

Ordinary Little Square Online Appendix Connection Matrix Causal Order Causal Discovery 
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 2012

Authors and Affiliations

  • Doris Entner
    • 1
  • Patrik O. Hoyer
    • 1
  1. 1.HIIT & Department of Computer ScienceUniversity of HelsinkiFinland

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