Transformer Is Inherently a Causal Learner
This work addresses the problem of causal discovery for researchers and practitioners by providing a scalable, interpretable method that outperforms existing algorithms, though it builds incrementally on transformer-based approaches.
The paper shows that autoregressively trained transformers inherently learn time-delayed causal structures from multivariate time series data, enabling causal graph recovery without explicit causal objectives, with performance surpassing state-of-the-art methods, especially as data heterogeneity increases.
We reveal that transformers trained in an autoregressive manner naturally encode time-delayed causal structures in their learned representations. When predicting future values in multivariate time series, the gradient sensitivities of transformer outputs with respect to past inputs directly recover the underlying causal graph, without any explicit causal objectives or structural constraints. We prove this connection theoretically under standard identifiability conditions and develop a practical extraction method using aggregated gradient attributions. On challenging cases such as nonlinear dynamics, long-term dependencies, and non-stationary systems, this approach greatly surpasses the performance of state-of-the-art discovery algorithms, especially as data heterogeneity increases, exhibiting scaling potential where causal accuracy improves with data volume and heterogeneity, a property traditional methods lack. This unifying view lays the groundwork for a future paradigm where causal discovery operates through the lens of foundation models, and foundation models gain interpretability and enhancement through the lens of causality.