Open Access
August 2020 Invariance, Causality and Robustness
Peter Bühlmann
Statist. Sci. 35(3): 404-426 (August 2020). DOI: 10.1214/19-STS721


We discuss recent work for causal inference and predictive robustness in a unifying way. The key idea relies on a notion of probabilistic invariance or stability: it opens up new insights for formulating causality as a certain risk minimization problem with a corresponding notion of robustness. The invariance itself can be estimated from general heterogeneous or perturbation data which frequently occur with nowadays data collection. The novel methodology is potentially useful in many applications, offering more robustness and better “causal-oriented” interpretation than machine learning or estimation in standard regression or classification frameworks.


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Peter Bühlmann. "Invariance, Causality and Robustness." Statist. Sci. 35 (3) 404 - 426, August 2020.


Published: August 2020
First available in Project Euclid: 11 September 2020

MathSciNet: MR4148216
Digital Object Identifier: 10.1214/19-STS721

Keywords: Anchor regression , causal regularization , distributional robustness , heterogeneous data , instrumental variables regression , interventional data , random forests , variable importance

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.35 • No. 3 • August 2020
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