在具有非恒定效应的因果可加模型中检验工具变量集的有效性
Testing the Validity of Instrumental Variable Sets in Causal Additive Models with Non-Constant Effects
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中文总结 AI 辅助
本文针对CAM-NCE下IV集有效性检验问题，提出CAT条件并扩展至含协变量场景，开发有限样本算法，经多类数据实验验证了方法的有效性与实用性。
中文摘要 AI 辅助
工具变量（IV）方法对于存在未观测混杂的因果效应估计具有强大作用，但在实践中，研究者常面临一组候选IV，其有效性难以仅从观测数据中确定。本文研究在具有非恒定效应的因果可加模型（CAM-NCE）下检验IV集有效性的问题。为解决该问题，我们提出一种可检验的条件，称为基于交叉辅助的独立性检验（CAT）条件，用于从观测数据评估IV集的有效性。我们证明，在完备性条件下，若违反CAT条件，对应集合不可能是有效IV集；此外，在交叉分布非退化条件下，我们确立CAT条件是CAM-NCE下刻画有效IV集的充要条件。随后，我们将CAT条件扩展到含协变量的场景，并开发了用于检验候选IV集有效性的实用有限样本算法。在合成数据及三个真实世界数据集上的大量实验，验证了所提方法的有效性和实用价值。
英文摘要
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.
