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Detecting non-causal artifacts in multivariate linear regression models
Dominik Janzing · Bernhard Schölkopf

Fri Jul 13 08:50 AM -- 09:00 AM (PDT) @ A5

We consider linear models where d potential causes X1,...,Xd are correlated with one target quantity Y and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes.We employ the idea that in the former case the vector of regression coefficients has `generic' orientation relative to the covariance matrix Sigma{XX} of X. Using an ICA based model for confounding, we show that both confounding and overfitting yield regression vectors that concentrate mainly in the space of low eigenvalues of Sigma{XX}.

Author Information

Dominik Janzing (Amazon Research Tübingen)
Bernhard Schölkopf (MPI for Intelligent Systems Tübingen, Germany)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

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