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Probabilistic latent variable models for distinguishing between cause and effect
Joris M Mooij · Oliver Stegle · Dominik Janzing · Kun Zhang · Bernhard Schölkopf

Wed Dec 08 12:00 AM -- 12:00 AM (PST) @ None #None

We propose a novel method for inferring whether X causes Y or vice versa from joint observations of X and Y. The basic idea is to model the observed data using probabilistic latent variable models, which incorporate the effects of unobserved noise. To this end, we consider the hypothetical effect variable to be a function of the hypothetical cause variable and an independent noise term (not necessarily additive). An important novel aspect of our work is that we do not restrict the model class, but instead put general non-parametric priors on this function and on the distribution of the cause. The causal direction can then be inferred by using standard Bayesian model selection. We evaluate our approach on synthetic data and real-world data and report encouraging results.

Author Information

Joris M Mooij (Radboud University Nijmegen)
Oliver Stegle (EMBL-EBI)
Dominik Janzing (MPI Tübingen)
Kun Zhang (Max Planck Institute for Biological Cybernetics)
Bernhard Schölkopf (MPI for Intelligent Systems)

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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