Large language models (LLMs) have shown impressive abilities across various domains, but can they truly understand cause and effect? New research challenges the assumption that LLMs can seamlessly process causal relationships, revealing intriguing insights into their strengths and limitations. The study introduces CausalGraph2LLM, a benchmark designed to evaluate how well LLMs understand causal graphs, which are visual representations of cause-and-effect links between variables. Researchers tested various LLMs, including GritLM, Mistral, Mixtral, GPT-3.5, Gemini, and GPT-4, by presenting them with causal graphs encoded in different textual formats like JSON, adjacency lists, and GraphML. They then asked the LLMs to answer questions about these graphs, such as identifying 'source' nodes (where causal chains begin) and 'mediator' nodes (intermediary steps in a causal chain). The results revealed that LLMs are surprisingly sensitive to *how* the causal graph is encoded. Performance varied significantly depending on the format used, even for powerful models like GPT-4 and Gemini. Interestingly, LLMs performed better when the causal graphs involved real-world concepts like medical diagnoses (from the 'Alarm' dataset) or insurance risk factors. This suggests that LLMs leverage their pre-existing knowledge from training data to make better sense of causal relationships. However, this reliance on background knowledge also introduces potential biases. For example, LLMs sometimes overestimated the number of causal connections, possibly influenced by pre-conceived notions embedded in their training data. The study also found that LLMs performed better on simpler, node-level queries (e.g., "Is node X a source?") compared to more complex graph-level queries (e.g., "List all source nodes"). This indicates that while LLMs can grasp individual causal links, they struggle more with understanding the overall causal structure of a system. Furthermore, some LLMs showed a tendency to overestimate causal links, while others were more conservative, highlighting the different biases present in various models. This research emphasizes the importance of carefully considering how causal information is presented to LLMs and the potential biases they might exhibit. As LLMs become increasingly integrated into tasks involving causal reasoning, understanding these limitations is crucial for developing more robust and reliable AI systems. The benchmark provides a valuable tool for future research in this crucial area, paving the way for improved LLM design and prompting strategies that better handle cause and effect.