因果推理是AI可靠性的关键，FM-CGM让零样本因果生成成为可能，做视觉生成或因果推理的研究者可以直接参考其框架设计。
因果生成建模对于开发可靠、透明的AI系统至关重要，但现有方法通常需要在训练时集成因果约束，缺乏利用预训练基础模型零样本推理能力的统一框架。本文提出FM-CGM，一个模块化框架，通过概念提取器、概念操纵器和反事实生成器三个核心组件，实现端到端的视觉因果推理。该框架利用大型推理模型进行因果推断，结合文本到图像扩散模型进行生成，支持零样本因果发现、干预和反事实生成。同时，作者开发了因果语义引导（CSG）机制，确保语义干预传播到后代概念并保留不变区域。实验表明，该方法能识别合理的因果结构，并生成忠实的反事实图像。
Leveraging Foundation Models for Causal Generative Modeling
Causal generative modeling is essential for developing reliable and transparent AI systems capable of counterfactual reasoning. While existing approaches focus on integrating causal constraints during the training of generative models, they often lack a unified framework to leverage the zero-shot reasoning capabilities of pretrained foundation models. We introduce FM-CGM, a modular framework for end-to-end visual causal reasoning using pretrained foundation models. FM-CGM formalizes the causal pipeline through three core components: a concept extractor, a concept manipulator, and a counterfactual generator. By leveraging a large reasoning model for causal inference and a text-to-image diffusion model for generation, our approach enables zero-shot causal discovery, intervention, and counterfactual generation. We then develop Causal Semantic Guidance (CSG), a cross-attention-based mechanism that ensures semantic interventions propagate to descendant concepts while preserving invariant regions. We empirically show that our approach can identify plausible causal structures and is suitable for faithful counterfactual image generation.