Robust Representation Learning through Explicit Environment Modeling

2026-04-28

AI Summary

This work addresses a critical limitation of traditional causal invariant representation learning, which fails when environmental variables directly influence the prediction target due to its reliance on the “no direct environmental effect” assumption. To overcome this constraint, the authors propose a novel paradigm that explicitly models distributional shifts across environments and marginalizes out environmental variables during representation learning. Grounded in a generalized random intercept model, the method provides theoretically analyzable guarantees on generalization performance. Extensive experiments across diverse and challenging multi-environment settings demonstrate that the approach significantly outperforms existing invariant learning methods, exhibiting superior robustness and effectiveness in out-of-distribution generalization.

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.

Problem

• robust representation learning
• environmental variation
• distribution shift
• causal invariance
• out-of-distribution generalization

Innovation

• environment modeling
• robust representation learning
• generalized random-intercept models
• distributional shift
• marginalization