arXiv:2602.14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions. Recently proposed Causal Foundation Models (CFMs) promise a more unified approach by amortising causal discovery and inference in a single step. However, in their current state, they do not allow for the incorporation of any domain knowledge, which can lead to suboptimal predictions. We bridge this gap by introducing methods to condition CFMs on causal information, such as the causal graph or more readily available ancestral information.
The rapid advancement of AI models and the increasing recognition of causality's importance in building robust, trustworthy AI systems necessitates the integration of domain knowledge into foundational models.
This development improves the utility and reliability of advanced AI systems by allowing them to incorporate crucial domain expertise, moving beyond purely data-driven approaches towards more intelligent and interpretable decision-making.
AI Foundation Models can now be conditioned with expert-derived causal information, potentially leading to more accurate predictions and inferences, especially in complex, real-world scenarios where data alone is insufficient.
- · AI researchers and developers
- · Industries relying on complex decision-making
- · Domain experts
- · Developers of 'black box' AI solutions
- · Purely data-driven approaches in critical applications
Causal Foundation Models will become more effective and widely adopted across various applications.
Increased trust and explainability in AI systems could accelerate their deployment in highly regulated or sensitive fields.
The enhanced capability of AI to model complex systems with domain knowledge could enable breakthroughs in scientific discovery and problem-solving.
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Read at arXiv cs.LG