让特征学习关注结果变量，提升隐藏混杂下的因果估计效果
Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
- 用包含结果信息的增强算子设计对比损失，使特征学习更关注任务
- 在谱不匹配情况下仍保持良好性能，验证了方法鲁棒性
- 适合处理存在隐藏混杂的因果推断问题，尤其当传统方法失效时
针对存在隐藏混杂时的因果效应估计问题，本文研究非参数工具变量回归。现有方法依赖于学习谱特征，即处理变量与工具变量间算子的主奇异子空间特征。然而这些特征与结果变量无关，当真实因果函数无法由主导奇异函数表示时，方法可能失效。为此，本文提出增强谱特征学习框架，通过引入包含结果信息的增强算子，构建新型对比损失，使特征学习过程具备结果感知能力。该方法在谱不匹配条件下仍能有效学习任务相关特征。本文提供理论分析，并在多个挑战性基准上验证了方法的有效性。
原文摘要 · Abstract (English)
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.
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