Testing the Validity of Instrumental Variable Sets in Causal Additive Models with Non-Constant Effects

2026-08-20

AI Summary

本文针对因果加性模型中工具变量集的有效性测试问题，提出了一种基于交叉辅助独立性测试（CAT）条件的方法，并通过实验证明了其有效性和实用性。

Abstract

Instrumental variable (IV) methods are powerful for causal effect estimation with unmeasured confounding, but in practice researchers often face a set of candidate IVs whose validity is difficult to determine from observational data. This paper studies the problem of testing the validity of IV sets under Causal Additive Models with Non-Constant Effects (CAM-NCE). To address this problem, we propose a testable condition, termed the Cross Auxiliary-based independence Test (CAT) condition, for assessing IV set validity from observational data. We show that, under the completeness condition, if the CAT condition is violated, the corresponding set cannot be a valid IV set. Furthermore, under a cross distributional non-degeneracy condition, we establish that the CAT condition becomes both necessary and sufficient for characterizing valid IV sets under CAM-NCE. We then extend the CAT condition to settings with covariates and develop a practical finite-sample algorithm for testing the validity of candidate IV sets. Extensive experiments on synthetic data and three real-world datasets demonstrate the effectiveness and practical utility of the proposed method.

Problem

• Instrumental Variable
• Causal Additive Models
• Non-Constant Effects
• Observational Data
• Validity Testing

Innovation

• Cross Auxiliary-based independence Test (CAT)
• Causal Additive Models with Non-Constant Effects (CAM-NCE)
• Instrumental Variable (IV)