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In the univariate case, we show that by comparing the individual complexities of univariate cause and effect, one can identify the cause and the effect, without considering their interaction at all.
Integral probability metrics and their generating classes of functions advances in applied probability
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Probabilistic latent variable models for distinguishing between cause and effect
Stegle, O., Janzing, D., Zhang, K., Mooij, J. M., and Schölkopf, B · 2010
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Distinguishing causes from effects using nonlinear acyclic causal models
Zhang, K. and Hyvärinen, A · 2010
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Directlingam: A direct method for learning a linear non-gaussian structural equation model
Shimizu, S., Inazumi, T., Sogawa, Y., Hyvärinen, A., Kawahara, Y., Washio, T., Hoyer, P. O., and Bollen, K · 2011
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Kernel-based conditional independence test and application in causal discovery
Zhang, K., Peters, J., Janzing, D., and Schölkopf, B · 2011
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Inferring deterministic causal relations
Daniusis, P., Janzing, D., Mooij, J. M., Zscheischler, J., Steudel, B., Zhang, K., and Schölkopf, B · 2012
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Invariant causal prediction for nonlinear models
Heinze-Deml, C., Peters, J., and Meinshausen, N · 2017
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Discovering causal signals in images
Lopez-Paz, D., Nishihara, R., Chintala, S., Schölkopf, B., and Bottou, L · 2017
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Telling cause from effect using mdl-based local and global regression
Marx, A. and Vreeken, J · 2017
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Elements of Causal Inference - Foundations and Learning Algorithms
Peters, J., Janzing, D., and Schölkopf, B · 2017
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Cause-effect inference by comparing regression errors
Bloebaum, P., Janzing, D., Washio, T., Shimizu, S., and Schoelkopf, B · 2018
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Learning functional causal models with generative neural networks
Goudet, O., Kalainathan, D., Caillou, P., Lopez-Paz, D., Guyon, I., and Sebag, M · 2018
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Towards a learning theory of cause-effect inference
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Conditional distribution variability measures for causality detection
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