用因果智能设计干预策略，精准推动系统状态转换
Causal Intelligence for Constraint-Aware Intervention Design to Induce State Transitions
- 基于因果图与结构因果模型，识别驱动状态变化的关键机制
- 在真实生物数据上实现目标状态转换，成功率高且干预策略可解释
- 适合需可解释干预方案的科研与生物医药领域研究者
通过针对性干预将系统从一种状态引导至另一状态是科学中的核心挑战，但多数预测模型缺乏机制性洞察，也无系统的决策框架。本文提出COAST（Causally Optimal Actions for State Transitions），一种用于模拟环境中设计约束性干预以实现用户定义状态转换的因果智能方法。给定源状态与目标状态的数据，COAST学习上下文相关的因果图与结构因果模型，将观测到的分布变化归因于机制级因果驱动因素，并引入一种新型约束感知多目标优化框架，平衡转换有效性、干预复杂度与目标状态稳定性。该方法模块化且领域无关，通过可替换组件整合特征选择、因果发现、因果建模、干预识别与评估。在合成基准与真实生物数据集上，COAST成功恢复关键因果驱动因子，识别出稳健的单靶点与多靶点干预策略，实现预期状态转换，并提供透明的机制性解释以指导实验验证。
原文摘要 · Abstract (English)
Driving a system from one state to another through targeted interventions is a fundamental challenge in science, yet most predictive models offer limited mechanistic insight and no principled framework for decision-making. Here we present COAST (Causally Optimal Actions for State Transitions), a causal-intelligence approach for the in-silico design of constrained interventions that induce user-defined state transitions. Given data characterizing source and target states, COAST learns context-specific causal graphs and structural causal models, attributes observed distributional shifts to mechanism-level causal drivers, and introduces a novel constraint-aware multi-objective optimization formulation that balances transition efficacy, intervention complexity, and target-state stability. The approach is modular and domain-agnostic, integrating feature selection, causal discovery, causal modeling, and intervention identification and evaluation through interchangeable components. Across synthetic benchmarks and real biological datasets, COAST recovers key causal drivers and identifies robust single- and multi-target intervention strategies that achieve desired state transitions, accompanied by transparent mechanistic rationales to guide experimental validation.
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