Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression

2025-11-30

AI Summary

In nonparametric instrumental variable (IV) regression under latent confounding, spectral methods often fail because the causal function cannot be well-represented by dominant singular functions of the IV operator, leading to spectral misalignment. Method: This paper proposes a response-aware enhanced spectral feature learning framework. It constructs an augmented IV operator and designs a contrastive loss that jointly models structural relationships among instruments, treatments, and outcomes, explicitly aligning spectral feature learning with the causal estimation objective. Contribution/Results: We establish theoretical consistency and convergence rates for the proposed estimator. Empirically, our method significantly improves estimation accuracy of the causal function across challenging benchmarks—including high-dimensional settings, weak instruments, and nonsmooth causal functions—outperforming existing spectral methods and deep IV baselines.

Technology Category

• Machine Learning: Causal Learning
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We address the problem of causal effect estimation in the presence of hidden confounders using nonparametric instrumental variable (IV) regression. An established approach is to use estimators based on learned spectral features, that is, features spanning the top singular subspaces of the operator linking treatments to instruments. While powerful, such features are agnostic to the outcome variable. Consequently, the method can fail when the true causal function is poorly represented by these dominant singular functions. To mitigate, we introduce Augmented Spectral Feature Learning, a framework that makes the feature learning process outcome-aware. Our method learns features by minimizing a novel contrastive loss derived from an augmented operator that incorporates information from the outcome. By learning these task-specific features, our approach remains effective even under spectral misalignment. We provide a theoretical analysis of this framework and validate our approach on challenging benchmarks.

Problem

• Estimating causal effects with hidden confounders
• Improving nonparametric instrumental variable regression
• Addressing spectral misalignment in feature learning

Innovation

• Augmented Spectral Feature Learning framework
• Minimizing contrastive loss with outcome information
• Learning task-specific features for causal estimation