- Journal Article
Rights / licenseIn Copyright - Non-Commercial Use Permitted
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. Show more
Journal / seriesStatistical Science
Pages / Article No.
PublisherInstitute of Mathematical Statistics
SubjectAnchor regression; causal regularization; distributional robustness; heterogeneous data; instrumental variables regression; interventional data; Random Forests; variable importance
Organisational unit03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
786461 - Statistics, Prediction and Causality for Large-Scale Data (EC)
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Is referenced by: https://doi.org/10.3929/ethz-b-000443281
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