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Journal Article

Causal Discovery with Continuous Additive Noise Models

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

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Peters, J., Mooij, J., Janzing, D., & Schölkopf, B. (2014). Causal Discovery with Continuous Additive Noise Models. Journal of Machine Learning Research, 15(1), 2009-2053. Retrieved from http://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models.pdf.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0025-B4D3-4
Abstract
We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional experiments, from which data are often not available. We show that if the observational distribution follows a structural equation model with an additive noise structure, the directed acyclic graph becomes identifiable from the distribution under mild conditions. This constitutes an interesting alternative to traditional methods that assume faithfulness and identify only the Markov equivalence class of the graph, thus leaving some edges undirected. We provide practical algorithms for finitely many samples, RESIT (regression with subsequent independence test) and two methods based on an independence score. We prove that RESIT is correct in the population setting and provide an empirical evaluation.