Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation

We present Causal-Adapter, a modular framework that adapts frozen text-to-image diffusion for counterfactual generation. Our method enables causal interventions on target attributes, consistently propagating their effects to causal dependents without altering the core identity of the image. In contrast to prior approaches that rely on prompt engineering without explicit causal mechanism, Causal-Adapter leverages structural causal modeling augmented with two attribute regularization strategies: prompt-aligned injection, which aligns causal attributes with textual embeddings for precise semantic control, and a conditioned token contrastive loss to disentangle attribute factors and reduce spurious correlations. Causal-Adapter achieves state-of-the-art performance on both synthetic and real-world datasets, with up to 91% MAE reduction on Pendulum for accurate attribute control and 87% FID reduction on ADNI for high-fidelity MRI generation. These results show that our approach enables robust, generalizable counterfactual editing with faithful attribute modification and strong identity preservation. Code, training data, and models are available on the project page.