Deconfounding and Causal Regularisation for Stability and External Validity

Peter Bühlmann and Domagoj Ćevid

International Statistical Review, 2020, vol. 88, issue S1, S114-S134

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.

Date: 2020
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https://doi.org/10.1111/insr.12426

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