Dynamic contrast-enhanced breast MRI is highly sensitive but difficult to interpret. We ask whether counterfactual edits produced by a latent diffusion model can reveal image cues linked to tumor presence while preserving anatomy. Starting from a latent diffusion backbone, we fine-tune the U-Net denoiser on the MAMA-MIA dataset and generate slice-level edits via DDIM inversion and null-text inversion. We study two operations: curtailment (suppression of tumor evidence) and exaggeration (amplification or addition of tumor-like features). Semantic impact is quantified with a frozen 3D nnU-Net trained on expert tumor masks. Across cohorts, curtailment yields a consistent 18–43% mean reduction in predicted tumor extent, whereas exaggeration is less reliable, highlighting an asymmetry between subtractive and additive edits. These results suggest diffusion-based counterfactuals provide interpretable, anatomy-preserving “what-if” views that complement saliency maps for model auditing, hypothesis generation, and training (Code and trained models are available at https://github.com/Luab/breast-mri-counterfactuals .)