## Causal Discovery with Continuous Additive Noise Models

*Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf*; 15(Jun):2009−2053, 2014.

### 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.

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