These frameworks, such as those developed by the UCL Center for Artificial Intelligence , integrate Large Language Models (LLMs) with causal discovery tools to generate graphs illustrating how different variables influence each other [5.4].
At its core, a causal agent is a "thing" with the power to change the world by causing an effect [20]. causal agent
For a claim of causation to be valid, there must be a correlation between the cause and effect, the effect must follow the cause chronologically, and there should be a plausible explanation for the process [11]. These frameworks, such as those developed by the
Researchers look for causal agents to determine if an intervention should be applied to the subject (like a vaccine) or the agent itself (like boiling contaminated water) [17]. Researchers look for causal agents to determine if
Specialized tools like MRAgent autonomously scan scientific papers to find potential exposure-outcome pairs and validate causal relationships in complex diseases [18]. 4. Comparison Table: Causal AI vs. Agentic AI Causal AI Agentic AI Primary Goal Understand why things happen. Take direct action to optimize performance. Output Insights, causal graphs, and reasoning. Autonomous adjustments and task execution. Human Role Uses insights to improve human decision-making. Provides high-level goals for the agent to achieve.
The most reliable way to identify a causal agent is through randomized controlled experiments (such as A/B tests), where one group receives a "treatment" from the agent and another does not [12]. 2. Applications in Artificial Intelligence