Probabilistic latent variable models for distinguishing between cause and effect

Mooij, J. M.; Stegle, O.; Janzing, D.; Zhang, K.; Schölkopf, B.

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Probabilistic latent variable models for distinguishing between cause and effect

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Mooij, J. M.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Stegle, O.
Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Janzing, D.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zhang, K.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Schölkopf, B.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Mooij, J. M., Stegle, O., Janzing, D., Zhang, K., & Schölkopf, B. (2011). Probabilistic latent variable models for distinguishing between cause and effect. In J. Lafferty, C. Williams, J. Shawe-Taylor, R. Zemel, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 23 (pp. 1687-1695). Red Hook, NY: Curran Associates, Inc. Retrieved from https://papers.nips.cc/paper/2010/hash/c850371fda6892fbfd1c5a5b457e5777-Abstract.html.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-4CD5-9

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