Invariant Causal Prediction for Nonlinear Models

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Authors Christina Heinze-Deml, Jonas Peters, Nicolai Meinshausen
Journal/Conference Name ArXiv e-prints
Paper Category
Paper Abstract An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system’s underlying causal structure. To this end, Invariant Causal Prediction (ICP) [1] has been proposed which learns a causal model exploiting the invariance of causal relations using data from different environments. When considering linear models, the implementation of ICP is relatively straightforward. However, the nonlinear case is more challenging due to the difficulty of performing nonparametric tests for conditional independence.
Date of publication 2017
Code Programming Language R
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