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Invariant Causal Prediction for Nonlinear Models

Author

Listed:
  • Heinze-Deml Christina
  • Meinshausen Nicolai

    (Seminar für Statistik, ETH Zurich, Zurich, Switzerland)

  • Peters Jonas

    (University of Copenhagen, Department of Mathematics, Copenhagen, Denmark)

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.

Suggested Citation

  • Heinze-Deml Christina & Meinshausen Nicolai & Peters Jonas, 2018. "Invariant Causal Prediction for Nonlinear Models," Journal of Causal Inference, De Gruyter, vol. 6(2), pages 1-35, September.
    • Handle: RePEc:bpj:causin:v:6:y:2018:i:2:p:35:n:2
    DOI: 10.1515/jci-2017-0016
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