Strong Correlations Induce Cause Only Predictions in Transformer Training

We revisit when Transformers can prioritize causes over spurious effects by viewing the problem through data correlation strength and the implicit regularization of gradient descent. We identify a phenomenon called Correlation Crowding-Out (CCO) arising from the training dynamics of Transformers. Specifically, under strongly correlated causal features, gradient descent filters out spurious cues and converges to a predictor that relies almost exclusively on the causes. Theoretically, using a simplified Transformer model trained on data from a minimal causal chain, we introduce a Dominant-coordinate condition that characterizes when CCO arises and explain its mechanism as a coupling of ''occupation'' and ''crowding-out''. ''Occupation'' denotes to rapid growth of weights aligned with the dominant causal direction while non-dominant directions remain small. ''Crowding-out'' denotes to attention logits align with separation directions favoring the causal branch, suppressing descendants. We provide convergence guarantees for both the optimization trajectory and generalization. Our empirical results on simulated and real examples across various tasks including vision and natural language demonstrate the procedure. Together, these results show that, under suitable conditions, standard training alone can induce cause only prediction.