Robust Representation Learning through Explicit Environment Modeling
TLDR
This paper proposes a robust representation learning method that explicitly models and marginalizes environment variation, outperforming causal invariant methods.
Key contributions
- Addresses robust learning where environment directly impacts the target, a limitation of causal invariance.
- Proposes explicitly modeling and marginalizing environment variation for robust representations.
- Introduces generalized random-intercept models as a concrete method for this approach.
- Demonstrates superior empirical performance over traditional invariant-learning methods.
Why it matters
This paper addresses a key limitation of causal invariant learning by providing a method that accounts for direct environmental effects on targets. This enables more robust and accurate predictions in complex real-world scenarios where current methods fall short.
Original Abstract
We consider learning from labeled data collected across multiple environments, where the data distribution may vary across these environments. This problem is commonly approached from a causal perspective, seeking invariant representations that retain causal factors while discarding spurious ones. However, this framework assumes that the environment has no direct effect on the target. In contrast, we consider settings in which this assumption fails, but still aim to learn representations that support robust prediction on average across previously unseen environments. To this end, we study representations learned by explicitly modeling variation across environments and then marginalizing that variation out. We analyze the resulting representations and characterize when they are preferable to those learned by causal invariant-representation methods. We propose a concrete method based on generalized random-intercept models, a class of predictors in which such marginalization is possible, and study their generalization properties. Empirically, we show that these models outperform invariant-learning methods across a range of challenging settings.
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