Can AI truly understand cause and effect? Large Language Models (LLMs) excel at many tasks, but causal reasoning has remained a major stumbling block. They often struggle to differentiate between mere correlation and actual causal relationships, sometimes giving near-random answers to causal questions. A new research paper introduces a novel framework called "Causal Chain of Prompting" (C²P) aimed at solving this problem. C²P guides LLMs through a series of steps, much like a detective solving a case. It first identifies the key variables involved, then figures out the connections between them based on the provided information. The framework then uses these connections to build a causal map, which helps the LLM deduce the true cause-and-effect relationships. Unlike previous attempts, C²P doesn't rely on external tools or vast amounts of extra data. Tests on standard benchmark datasets, real-world narratives, and even complex astrophysics data show C²P significantly boosts the causal reasoning abilities of both GPT-4 Turbo and LLaMA 3.1 models. By providing a few examples of how to use C²P, the researchers demonstrated impressive improvements in accuracy, suggesting current LLMs have more causal reasoning potential than previously thought. This research marks an exciting step toward more robust, logical AI systems that can truly understand 'why?' While challenges remain, particularly with indirect causal links and many variables, C²P paves the way for LLMs that could revolutionize fields dependent on causal insights, from healthcare and finance to scientific discovery itself.