RAMEN identifies treatment effects from multiple environments in a doubly robust manner by leveraging data heterogeneity without requiring the causal graph.
Causality-oriented robustness: exploiting general additive interventions
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In linear regression for supervised domain adaptation, causal invariance yields finite-sample gains only when target-risk margins exceed estimation error, with matching upper and lower bounds derived and connected to structural shifts.
An empirical Bayes variational inference method learns environment-robust latent variables from multi-environment data for improved prediction in unseen environments.
citing papers explorer
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Doubly robust identification of treatment effects from multiple environments
RAMEN identifies treatment effects from multiple environments in a doubly robust manner by leveraging data heterogeneity without requiring the causal graph.
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How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
In linear regression for supervised domain adaptation, causal invariance yields finite-sample gains only when target-risk margins exceed estimation error, with matching upper and lower bounds derived and connected to structural shifts.
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Environment-Robust Representation Learning with Empirical Bayes
An empirical Bayes variational inference method learns environment-robust latent variables from multi-environment data for improved prediction in unseen environments.