This paper describes the multi-strategy ensemble approach that has been used to develop the model submitted to the Abductive Event Reasoning shared task. The proposed model combines semantic similarity, causal pattern recognition, and Large Language Models (LLMs) to identify causal relationships between news events and their causes. Our system achieved competitive performance by integrating semantic embedding-based similarity, explicit causal pattern matching, keyword overlap analysis, temporal alignment scoring, and LLM-enhanced reasoning. Our system achieved accuracies of 65.4\% and 43.2\% on the development set using the LLM-enhanced configuration and the non-LLM ensemble, respectively. The final score using the test set on the leaderboard is 0.3.