Transformer Is Inherently a Causal Learner

We reveal that decoder-only 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, we see this approach greatly surpass the performance of state-of-the-art discovery algorithms, especially as data heterogeneity increases, exhibiting scaling laws where causal accuracy improves with data volume, a property traditional methods lack. This unifying view opens a new paradigm where causal discovery operates through the lens of foundation models, and foundation models gain interpretability and enhancement through the lens of causality.