Kernelized IV Confounding Screen for Testing Conditional Exogeneity

Instrumental variables (IVs) provide a powerful route to causal identification when latent confounding is present, and conditional exogeneity is one of their most basic credibility requirements. Existing methods for testing IV conditional exogeneity typically rely on structural restrictions, such as linearity or discrete-support conditions, or require auxiliary side information, such as assignment mechanisms and negative controls. We therefore propose Kernelized IV Confounding Screen (KICS) for testing IV conditional exogeneity under more flexible structural conditions and without requiring auxiliary side information. The key idea of KICS is to compare the coefficient systems of an ordinary kernel regression and a candidate-IV-induced weighted kernel regression after removing the part of the candidate IV explained by observed covariates. Theoretically, at the population level, the two coefficient systems coincide under the null hypothesis and differ under detectable alternatives; the estimated coefficient discrepancy is asymptotically normal, enabling a Wald-type test for practical inference. Experiments in synthetic and real-data-based settings show that KICS detects conditional exogeneity violations in flexible nonlinear settings, remains competitive in settings favorable to specialized baselines, and yields real-data results consistent with prior IV assessments. Overall, KICS provides a practical approach to IV conditional exogeneity testing in settings where existing methods are difficult to apply.