Causal Robustness from Synthetic Environments

Key points

• Causality-oriented methods for robust prediction usually use heterogeneity across observed environments both to learn an invariant prediction function and to determine the perturbations against which it is robust.1
• We show that both can be recovered from one distribution under a structural additive-noise mechanism for the response.1
• We generate synthetic environments by reweighting the covariate distribution along the prediction and use a Cramér--von Mises criterion to measure departures from this invariance.1
• Under an additional orthogonality condition, we identify the causal prediction function as the unique risk minimiser subject to this invariance restriction.1

Sources (1)

• [1]Causal Robustness from Synthetic Environments
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 12:10 PM

  We study robust prediction when the training data come from a single observational distribution.

  Causality-oriented methods for robust prediction usually use heterogeneity across observed environments both to learn an invariant prediction function and to determine the perturbations against which it is robust.

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