Leveraging Foundation Models for Causal Generative Modeling
Aneesh Komanduri, Xintao Wu
Why It Matters
What makes this one worth your time
This research addresses the critical need for reliable AI systems capable of counterfactual reasoning, which is essential for applications in decision-making and interpretability.
FM-CGM enables zero-shot causal reasoning using foundation models.
Summary
The paper introduces FM-CGM, a modular framework for causal generative modeling that utilizes pretrained foundation models to facilitate zero-shot causal discovery and counterfactual generation through a structured pipeline.
Key contributions
- Introduction of FM-CGM as a modular framework for visual causal reasoning.
- Development of Causal Semantic Guidance (CSG) for ensuring semantic interventions propagate correctly.
- Empirical demonstration of the framework's ability to identify plausible causal structures.
Notable insights
- The integration of a cross-attention mechanism for semantic intervention propagation is a clever approach to maintaining causal relationships.
- The use of a text-to-image diffusion model for counterfactual generation highlights an innovative application of multimodal models in causal inference.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.23861v1 Announce Type: cross Abstract: 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.