This academic paper introduces PC-SUBQ, a prompting strategy designed to improve the ability of Large Language Models (LLMs) to infer causal relationships from correlations. The strategy breaks down the complex task into sequential sub-questions that mirror the steps of the PC algorithm, a formal causal discovery method. Evaluating PC-SUBQ on the CORR2CAUSE benchmark, which provides correlation data to test causal reasoning, the researchers found that it significantly boosted performance across several LLMs compared to baseline prompting techniques. The approach also demonstrated robustness to variations in variable names and paraphrased phrasing, suggesting it enhances the models' underlying causal reasoning skills rather than just memorization. PC-SUBQ offers transparent reasoning steps, making it easier to understand where errors occur.