Causal Semantic Guidance

FM-CGM: Foundation Model Framework for Zero-Shot Visual Causal Generative Modeling

FM-CGM is a modular framework that decomposes visual causal reasoning into three components—concept extractor, concept manipulator, and counterfactual generator—using pretrained foundation models without task-specific causal training. The approach combines a large reasoning model for causal inference with a text-to-image diffusion model for generation, enabling zero-shot causal discovery and counterfactual image synthesis. A novel cross-attention mechanism called Causal Semantic Guidance (CSG) ensures that semantic interventions propagate correctly through causal descendants while preserving unaffected image regions. Empirical results show the framework can identify plausible causal structures and generate faithful counterfactual images.