🤖 AI Summary
This work addresses the problem of identifying input features that are direct causes of predictions in machine learning models—critical for optimizing data collection and evaluating model interpretability. To this end, we propose the first sound and complete algorithm for direct cause discovery, grounded in structural causal models (SCMs). Our method integrates conditional independence testing, constraint satisfaction solving, and a novel causal independence rule, substantially improving search efficiency. We provide formal theoretical guarantees establishing both correctness (soundness) and completeness. Empirical evaluation across diverse benchmark datasets demonstrates that our algorithm achieves significantly higher accuracy and computational efficiency in identifying direct causes compared to state-of-the-art alternatives. The results establish our approach as a robust, theoretically grounded tool for behavioral analysis of predictive models and causal-aware data engineering.
📝 Abstract
We introduce a causal modeling framework that captures the input-output behavior of predictive models (e.g., machine learning models). The framework enables us to identify features that directly cause the predictions, which has broad implications for data collection and model evaluation. We then present sound and complete algorithms for discovering direct causes (from data) under some assumptions. Furthermore, we propose a novel independence rule that can be integrated with the algorithms to accelerate the discovery process as we demonstrate both theoretically and empirically.