Foundation models are large artificial intelligence (AI) systems trained on vast amounts of data to perform a wide range of tasks, including answering questions, generating content, and assisting decision-making. These models are increasingly used in areas that affect everyday life, such as healthcare, education, and environmental planning. However, despite their impressive capabilities, they often rely on patterns and correlations in data rather than true causal relationships. This limitation can lead to unreliable or misleading outputs, especially in high-stakes situations. For example, a model may incorrectly assume that one factor causes another simply because they frequently appear together in data. This project addresses this critical challenge by enabling foundation models to better understand and use causal knowledge, which describes how one factor directly influences another in the real world. By improving the ability of these models to reason about cause and effect, the project aims to make them more reliable, transparent, and aligned with human reasoning. The results will support safer and more effective use of AI in important societal domains, strengthen decision-making in complex environments, and contribute to education and workforce development by training students in emerging areas of trustworthy AI. This project develops a systematic framework for understanding, leveraging, editing, and applying causal knowledge in foundation models, organized into four co