解决因果推断中分组不一致导致的预测偏差问题。
Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation
- 提出跨分组标签一致性机制，消除同一实例在不同分组下的预测矛盾。
- 实验表明，该方法在合成与真实数据集上均显著降低测试误差。
- 适用于所有已有CATE估计算法，特别适合需要高稳定性的医疗决策场景。
众多条件平均处理效应（CATE）估计算法存在一个共性问题：同一实例在不同分组分配下表现出不一致的学习行为。本文首次提出量化并可视化这种不一致性的度量方法，并通过理论分析证明该不一致性会带来更高的测试误差，且无法通过传统机器学习手段解决。为此，我们提出一种通用方法——跨分组标签一致性（CLAGA），可消除此类不一致，兼容任何现有CATE算法。在合成数据和真实世界数据集上的实验表明，引入CLAGA后模型性能显著提升。
原文摘要 · Abstract (English)
Numerous algorithms have been developed for Conditional Average Treatment Effect (CATE) estimation. In this paper, we first highlight a common issue where many algorithms exhibit inconsistent learning behavior for the same instance across different group assignments. We introduce a metric to quantify and visualize this inconsistency. Next, we present a theoretical analysis showing that this inconsistency indeed contributes to higher test errors and cannot be resolved through conventional machine learning techniques. To address this problem, we propose a general method called \textbf{Consistent Labeling Across Group Assignments} (CLAGA), which eliminates the inconsistency and is applicable to any existing CATE estimation algorithm. Experiments on both synthetic and real-world datasets demonstrate significant performance improvements with CLAGA.
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