一种基于因果关系的通用神经网络 (CINN) 方法,用于定量风险分析和决策支持
Xiaoge Zhang1, Xiangyun Long2, Yu Liu3
1Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
概括
本研究引入了一个将因果知识编码到神经网络中的框架,通过因果意识推理实现更好的风险分析和决策支持. 开发的因果关系信息神经网络 (CINN) 促进了强大的"如果"分析,以便明智地做出决策.
科学领域:
- 人工智能的人工智能
- 因果推理因果推理
- 机器学习 机器学习
背景情况:
- 有效的风险分析和决策支持需要理解复杂的因果关系.
- 现有的方法往往难以将定性或定量因果知识整合到预测模型中.
研究的目的:
- 开发一种通用框架,将层次性因果知识编码到神经网络中.
- 促进合理的风险分析和决策支持,使用因果意识干预推理.
- 引入因果关系信息神经网络 (CINN).
主要方法:
- 通过数据或专家的指向非循环图 (DAG) 来发现因果知识.
- 将神经网络架构和损失函数与因果结构对齐.
- 将领域知识纳入作为约束,以确保稳定的因果关系.
- 使用训练有素的CINN进行干预推理和"如果"分析.
主要成果:
- 建立CINN的四步程序,整合因果结构和领域知识.
- 已证明CINN能够进行政策和行动影响估计的干预推理.
- 通过案例研究表明,CINN在风险分析和决策支持方面的实质性好处.
结论:
- 拟议的CINN框架有效地将因果知识整合到神经网络中.
- 通过实现因果意识干预推理,CINN增强了风险分析和决策支持.
- 该方法提供了一个强大的方法来构建可解释和可靠的AI系统.
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