Transforming Causality: Transformer-Based Temporal Causal Discovery with Prior Knowledge Integration
This work addresses causal discovery in time-series data for researchers and practitioners, representing a strong incremental advance with specific performance gains.
The paper tackles the problem of temporal causal discovery by addressing complex nonlinear dependencies and spurious correlations, achieving a 12.8% improvement in F1-score for causal discovery and 98.9% accuracy in estimating causal lags.
We introduce a novel framework for temporal causal discovery and inference that addresses two key challenges: complex nonlinear dependencies and spurious correlations. Our approach employs a multi-layer Transformer-based time-series forecaster to capture long-range, nonlinear temporal relationships among variables. After training, we extract the underlying causal structure and associated time lags from the forecaster using gradient-based analysis, enabling the construction of a causal graph. To mitigate the impact of spurious causal relationships, we introduce a prior knowledge integration mechanism based on attention masking, which consistently enforces user-excluded causal links across multiple Transformer layers. Extensive experiments show that our method significantly outperforms other state-of-the-art approaches, achieving a 12.8% improvement in F1-score for causal discovery and 98.9% accuracy in estimating causal lags.