Open access
Date
2022-06Type
- Journal Article
Abstract
Prediction models often fail if train and test data do not stem from the same distribution. Out-of-distribution (OOD) generalization to unseen, perturbed test data is a desirable but difficult-to-achieve property for prediction models and in general requires strong assumptions on the data generating process (DGP). In a causally inspired perspective on OOD generalization, the test data arise from a specific class of interventions on exogenous random variables of the DGP, called anchors. Anchor regression models, introduced by Rothenhausler et al. (J R Stat Soc Ser B 83(2):215-246, 2021. haps://doi.org/10.1111/rssb.12398), protect against distributional shifts in the test data by employing causal regularization. However, so far anchor regression has only been used with a squared-error loss which is inapplicable to common responses such as censored continuous or ordinal data. Here, we propose a distributional version of anchor regression which generalizes the method to potentially censored responses with at least an ordered sample space. To this end, we combine a flexible class of parametric transformation models for distributional regression with an appropriate causal regularizer under a more general notion of residuals. In an exemplary application and several simulation scenarios we demonstrate the extent to which OOD generalization is possible. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000548901Publication status
publishedExternal links
Journal / series
Statistics and ComputingVolume
Pages / Article No.
Publisher
SpringerSubject
Anchor regression; Covariate shift; Diluted causality; Distributional regression; Transformation models; Out-of-distribution generalizationOrganisational unit
03502 - Bühlmann, Peter L. / Bühlmann, Peter L.
Funding
786461 - Statistics, Prediction and Causality for Large-Scale Data (EC)
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