Detecting Concept Drift in Data Streams Using Instrumental Variables

Concept drift is a common phenomenon in real-world data streams that requires models to adapt to changing data distributions. Yet, most concept drift detectors raise flags according to changes in observable distributions or predictive error rates, often monitoring proxy signals, and requiring continuous access to ground-truth labels. In addition, they do not provide insight into why or how the data-generating process changed. Although recent work frames concept drift as changes in underlying causal mechanisms, identifying these mechanisms shifts from observational data is challenging due to unobserved confounding. In this work, we track mechanism changes in continuous data streams using Instrumental Variables (IVs). Specifically, we introduce an online IV monitoring framework that assumes an initial causal graph to identify valid instruments and continuously estimates their associated causal effects using the Wald estimator. We demonstrate two complementary applications of this framework. First, the Instrumental Variable-monitored Drift Detector (IVDD) compares successive IV estimates to detect and localize changes in monitored causal relationships without ground-truth labels. Second, the Instrumental Variable-monitored Adaptive Tree (IVAT) incorporates local IV-based causal-effect estimates into online tree learning, using causal-effect heterogeneity to identify relevant subpopulations and local monitoring to guide targeted adaptation. Empirical evaluations on synthetic structural causal models and real-world streams demonstrate that our IV-monitoring approaches pinpoint changes in causal mechanisms, achieving competitive performance against state-of-the-art drift detectors in delayed-feedback scenarios. Source code is available on GitHub.