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Deconfounding and Causal Regularisation for Stability and External Validity

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  • Peter Bühlmann
  • Domagoj Ćevid

Abstract

We review some recent works on removing hidden confounding and causal regularisation from a unified viewpoint. We describe how simple and user‐friendly techniques improve stability, replicability and distributional robustness in heterogeneous data. In this sense, we provide additional thoughts on the issue of concept drift, raised recently by Efron, when the data generating distribution is changing.

Suggested Citation

  • Peter Bühlmann & Domagoj Ćevid, 2020. "Deconfounding and Causal Regularisation for Stability and External Validity," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 114-134, December.
    • Handle: RePEc:bla:istatr:v:88:y:2020:i:s1:p:s114-s134
    DOI: 10.1111/insr.12426
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    References listed on IDEAS

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