Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection
The study introduces a Causally Guided Transformer (CGT) for multivariate time-series anomaly detection by integrating a causal graph prior with deep learning. It restricts predictions to causally supported features using parent masks, uses a shadow path to suppress unreliable correlations, and identifies root causes via counterfactual clamping. This method achieves state-of-the-art performance on benchmarks while improving interpretability.