The article titled "Theoretical Guarantees for Causal Discovery on Large Random Graphs," published on arXiv, investigates the false-negative rate in causal discovery within the context of large random graphs. Specifically, it examines how accurately true causal edges can be detected under certain conditions, focusing on sparse Erdős–Rényi directed acyclic graphs. This research contributes to understanding the reliability of causal discovery methods when applied to complex network structures characterized by randomness and sparsity. By providing theoretical guarantees, the study aims to clarify the limitations and strengths of causal inference techniques in identifying genuine causal relationships. The emphasis on false negatives highlights the importance of minimizing missed causal connections, which is critical for applications relying on accurate causal models. This work fits within a broader set of recent studies on causal discovery in machine learning, as indicated by related arXiv publications. Overall, the article advances foundational knowledge in causal inference by addressing performance guarantees in a mathematically rigorous framework.