细胞癌变、草原荒漠化等复杂系统状态转变具突发性与不可逆性,其机制解析与早期预警是跨领域共性难题。现有方法无法刻画状态跃迁的因果结构重构特性,本文提出“状态驱动的因果比较”新范式,实现从“单一状态因果描述”到“多状态间因果结构差异比较”的转变。基于该范式构建跨状态因果差异分析框架及数学优化模型,核心创新是将状态间因果网络结构差异确立为可量化核心对象,突破传统静态指标事后比较局限。进一步提出“节点因果差异贡献度”指标识别关键变量,以高脂血症（血脂异常）为实证,对比健康、临界、疾病状态的血脂代谢因果网络并建立早期预测模型。模型在外部独立数据集上表现优于经典方法,表明了所提方法的科学性与实用性。
State transitions in complex systems (e.g., cellular carcinogenesis, grassland desertification) exhibit suddenness and irreversibility. Deciphering their underlying mechanisms and enabling early warning constitute a cross-disciplinary common challenge. Existing methods struggle to characterize causal reconstruction during state transitions. To address this limitation, this study proposes a new paradigm of state-driven causal comparison, realizing a perspective shift from causal description of a single state to comparative analysis of causal structural differences between states. A cross-state causal difference analysis framework and a mathematical optimization model are constructed. The core innovation lies in establishing the structural differences of causal networks between states as a measurable direct object, breaking through the limitation of ex-post comparison of traditional static indicators. On this basis, a node contribution index of causal differences is proposed to identify key driving variables. Taking dyslipidemia as an example, by constructing and comparing causal networks of healthy, critical, and disease states, the developed early prediction model demonstrates superior predictive performance and generalization ability over classical methods on external data, verifying the effectiveness of the proposed approach.
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