A generic causality-informed neural network (CINN) methodology for quantitative risk analytics and decision support.

IF 3 3区 医学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Risk Analysis Pub Date : 2024-11-01 Epub Date: 2024-06-08 DOI:10.1111/risa.14347
Xiaoge Zhang, Xiangyun Long, Yu Liu, Kai Zhou, Jinwu Li
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引用次数: 0

Abstract

In this paper, we develop a generic framework for systemically encoding causal knowledge manifested in the form of hierarchical causality structure and qualitative (or quantitative) causal relationships into neural networks to facilitate sound risk analytics and decision support via causally-aware intervention reasoning. The proposed methodology for establishing causality-informed neural network (CINN) follows a four-step procedure. In the first step, we explicate how causal knowledge in the form of directed acyclic graph (DAG) can be discovered from observation data or elicited from domain experts. Next, we categorize nodes in the constructed DAG representing causal relationships among observed variables into several groups (e.g., root nodes, intermediate nodes, and leaf nodes), and align the architecture of CINN with causal relationships specified in the DAG while preserving the orientation of each existing causal relationship. In addition to a dedicated architecture design, CINN also gets embodied in the design of loss function, where both intermediate and leaf nodes are treated as target outputs to be predicted by CINN. In the third step, we propose to incorporate domain knowledge on stable causal relationships into CINN, and the injected constraints on causal relationships act as guardrails to prevent unexpected behaviors of CINN. Finally, the trained CINN is exploited to perform intervention reasoning with emphasis on estimating the effect that policies and actions can have on the system behavior, thus facilitating risk-informed decision making through comprehensive "what-if" analysis. Two case studies are used to demonstrate the substantial benefits enabled by CINN in risk analytics and decision support.

用于定量风险分析和决策支持的通用因果信息神经网络 (CINN) 方法。
在本文中,我们开发了一个通用框架,用于将以分层因果关系结构和定性(或定量)因果关系形式体现的因果知识系统地编码到神经网络中,从而通过因果感知干预推理促进合理的风险分析和决策支持。我们提出的建立因果信息神经网络(CINN)的方法分为四个步骤。第一步,我们阐述了如何从观测数据中发现或从领域专家那里获得有向无环图(DAG)形式的因果知识。接下来,我们将构建的 DAG 中代表观测变量之间因果关系的节点分为几组(如根节点、中间节点和叶节点),并根据 DAG 中指定的因果关系调整 CINN 的架构,同时保留每个现有因果关系的方向。除了专门的架构设计,CINN 还体现在损失函数的设计上,中间节点和叶节点都被视为 CINN 要预测的目标输出。第三步,我们建议在 CINN 中加入关于稳定因果关系的领域知识,而注入的因果关系约束就像护栏一样,可以防止 CINN 出现意外行为。最后,利用训练有素的 CINN 进行干预推理,重点是估计政策和行动对系统行为可能产生的影响,从而通过全面的 "假设 "分析,促进风险知情决策。两个案例研究证明了 CINN 在风险分析和决策支持方面的巨大优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Risk Analysis
Risk Analysis 数学-数学跨学科应用
CiteScore
7.50
自引率
10.50%
发文量
183
审稿时长
4.2 months
期刊介绍: Published on behalf of the Society for Risk Analysis, Risk Analysis is ranked among the top 10 journals in the ISI Journal Citation Reports under the social sciences, mathematical methods category, and provides a focal point for new developments in the field of risk analysis. This international peer-reviewed journal is committed to publishing critical empirical research and commentaries dealing with risk issues. The topics covered include: • Human health and safety risks • Microbial risks • Engineering • Mathematical modeling • Risk characterization • Risk communication • Risk management and decision-making • Risk perception, acceptability, and ethics • Laws and regulatory policy • Ecological risks.
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