When Shift Happens - Confounding Is to Blame

2025-05-27

AI Summary

This work identifies distributional shifts in latent confounders as a critical cause of failure for existing out-of-distribution (OOD) generalization methods, challenging the prevailing assumption that causal invariant representations necessarily outperform empirical risk minimization (ERM). Through rigorous theoretical analysis and empirical validation, we systematically demonstrate how confounder shifts violate the fundamental assumptions underlying invariant learning. To address this, we propose an environment-aware modeling framework that incorporates proxy variables for latent confounders to enable causal correction, thereby unifying ERM with confounder identification techniques. Experiments show that our method significantly outperforms state-of-the-art OOD generalization algorithms under confounder shift; notably, standard ERM can surpass invariant-learning approaches in such settings. Furthermore, we establish a novel criterion for covariate selection grounded in confounder identifiability, offering principled guidance for feature engineering in causal OOD generalization.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Distribution shifts introduce uncertainty that undermines the robustness and generalization capabilities of machine learning models. While conventional wisdom suggests that learning causal-invariant representations enhances robustness to such shifts, recent empirical studies present a counterintuitive finding: (i) empirical risk minimization (ERM) can rival or even outperform state-of-the-art out-of-distribution (OOD) generalization methods, and (ii) its OOD generalization performance improves when all available covariates, not just causal ones, are utilized. Drawing on both empirical and theoretical evidence, we attribute this phenomenon to hidden confounding. Shifts in hidden confounding induce changes in data distributions that violate assumptions commonly made by existing OOD generalization approaches. Under such conditions, we prove that effective generalization requires learning environment-specific relationships, rather than relying solely on invariant ones. Furthermore, we show that models augmented with proxies for hidden confounders can mitigate the challenges posed by hidden confounding shifts. These findings offer new theoretical insights and practical guidance for designing robust OOD generalization algorithms and principled covariate selection strategies.

Problem

• Understanding hidden confounding's impact on distribution shifts
• Challenging causal-invariant assumptions in OOD generalization
• Proposing proxy-augmented models for robust generalization

Innovation

• ERM rivals OOD methods with covariates
• Learn environment-specific not invariant relationships
• Proxies for hidden confounders mitigate shifts