Inferring deterministic causal relations

Daniusis, P; Janzing, D; Mooij, J; Zscheischler, J; Steudel, B; Zhang, K; Schölkopf, B

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Inferring deterministic causal relations

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Daniusis, P
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Janzing, D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Mooij, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zscheischler, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zhang, K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://event.cwi.nl/uai2010/papers/UAI2010_0121.pdf
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UAI2010-Daniusis_[0].pdf
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Citation

Daniusis, P., Janzing, D., Mooij, J., Zscheischler, J., Steudel, B., Zhang, K., et al. (2010). Inferring deterministic causal relations. In P. Grünwald, & P. Spirtes (Eds.), 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010) (pp. 143-150). Corvallis, OR, USA: AUAI Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BF3C-6

Abstract

We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints
to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We
provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.