In the present era, the proliferation and widespread adoption of Large Language Models (LLMs) have substantially elevated the capacity for understanding and reasoning in various textual tasks. Yet, there is a current shortage of effective methodologies for tackling Deliberative Questions, which require thoughtful consideration and may encompass evolving scenarios. To bridge this gap, this paper introduces a systematic approach that employs relevant impact analysis to build a cognitive framework, whose results guide LLMs to assist generate hypotheses and testing, known as Abductive Reasoning, for addressing deliberative questions, with the goal of fostering a deliberative thinking mindset. Our comprehensive evaluation on deliberative questions gathered from Reddit and Zhihu showed that pipeline generates superior answers in 65.5% and 73.6% of the cases on our datasets in ChatGPT and QwenMAX view, particularly excelling with an 88.3% dominance rate in addressing reasoning-type questions and and 84.4% in predictive-type questions.