Root cause analysis (RCA) identifies the faults and vulnerabilities underlying software failures, informing better design and maintenance decisions. Earlier approaches typically framed RCA as a classification task, predicting coarse categories of root causes. With recent advances in large language models (LLMs), RCA can be treated as a generative task that produces natural language explanations of faults. We introduce RCEGen, a framework that leverages state-of-the-art open-source LLMs to generate root cause explanations (RCEs) directly from bug reports. Using 298 reports, we evaluated five LLMs in conjunction with human developers and LLM judges across three key aspects: correctness, clarity, and reasoning depth. Qwen2.5-Coder-Instruct achieved the strongest performance (correctness ≈ 0.89, clarity ≈ 0.88, reasoning ≈ 0.65, overall ≈ 0.79), and RCEs exhibited high semantic fidelity (CodeBERTScore ≈ 0.98) to developer-written references despite low lexical overlap. The results demonstrated that LLMs achieve high accuracy in root cause identification from bug report titles and descriptions, particularly when reports contained error logs and reproduction steps.