Introducing causal inference in the energy-efficient building design process
将因果推断引入节能建筑设计过程AI 解读 ↓
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将因果推断引入节能建筑设计过程
- 研究问题
- 在节能建筑设计的假设情景决策中，如何识别设计参数之间的依赖，整合领域知识与数据，并明确模型解释及决策适用边界？
- 方法与思路
- 作者区分经验与原则性知识，提出通过干预识别因果图的四步流程，以因果图连接知识与数据，并用模拟及可行性研究说明。
- 材料陈述的贡献
- 摘要称该流程为参数依赖分析提供严谨且计算高效的框架，通过因果图增强解释与领域经验检验，并展示实现的可行性。
- 阅读关注点
- 核对四步流程的具体内容、干预如何实施与支持识别，以及知识输入、模拟估计量和可行性研究的验证范围及计算成本。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明完整识别条件、真实建筑节能效果或效率数值，可行性展示不等于效果验证。
原文摘要
"What-if" questions are intuitively generated and commonly asked during the design process. Engineers and architects need to inherently conduct design decisions, progressing from one phase to another. They either use empirical domain experience, simulations, or data-driven methods to acquire consequential feedback. We take an example from an interdisciplinary domain of energy-efficient building design to argue that the current methods for decision support have limitations or deficiencies in four aspects: parametric independency identification, gaps in integrating knowledge-based and data-driven approaches, less explicit model interpretation, and ambiguous decision support boundaries. In this study, we first clarify the nature of dynamic experience in individuals and constant principal knowledge in design. Subsequently, we introduce causal inference into the domain. A four-step process is proposed to discover and analyze parametric dependencies in a mathematically rigorous and computationally efficient manner by identifying the causal diagram with interventions. The causal diagram provides a nexus for integrating domain knowledge with data-driven methods, providing interpretability and testability against the domain experience within the design space. Extracting causal structures from the data is close to the nature design reasoning process. As an illustration, we applied the properties of the proposed estimators through simulations. The paper concludes with a feasibility study demonstrating the proposed framework's realization.