Causal Agent -

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].

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 causal agent

Unlike standard AI which is often reactive, Agentic AI with causal understanding can anticipate the consequences of its actions and identify the true mechanisms behind data trends (e.g., recognizing that "stress" is the real cause of weight gain during exams, not the exams themselves) [25, 35]. These frameworks, such as those developed by the

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. Comparison Table: Causal AI vs

A is an entity or force responsible for producing a specific effect or outcome. In various fields, it serves as the "bridge" between an initial condition and a final result. 1. General Concepts

By encoding causal links into their decision-making processes, AI agents can navigate complex environments more safely and handle "distribution shifts" (changes in environment rules) more effectively [22, 10]. 3. Causal Agents in Health and Science