论文标题
因果关系的因果发现和因果关系，以进行防火性评估：合并领域知识
Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge
论文作者
论文摘要
实验仍然是建立对火灾相关现象的理解的黄金标准。设计测试的主要目标是揭示数据生成过程（即，我们看到的观察结果是如何和原因）；或只是导致这种观察的原因。揭开这样的过程不仅可以提高我们的知识，而且还为我们提供了能够准确预测现象的能力。本文提出了一种利用因果发现和因果推断来评估结构成员的火力阻力的方法。在这种方法中，采用因果发现算法来揭示与钢筋混凝土（RC）柱的耐火性变量之间的因果结构。然后，将伴随推理算法应用于推断（估计）每个变量对特定干预的影响。最后，这项研究结束于将算法的因果发现与从领域知识和传统机器学习中获得的算法发现。我们的发现清楚地表明了将因果关系采用到我们领域的潜力和优点。
Experiments remain the gold standard to establish an understanding of fire-related phenomena. A primary goal in designing tests is to uncover the data generating process (i.e., the how and why the observations we see come to be); or simply what causes such observations. Uncovering such a process not only advances our knowledge but also provides us with the capability to be able to predict phenomena accurately. This paper presents an approach that leverages causal discovery and causal inference to evaluate the fire resistance of structural members. In this approach, causal discovery algorithms are adopted to uncover the causal structure between key variables pertaining to the fire resistance of reinforced concrete (RC) columns. Then, companion inference algorithms are applied to infer (estimate) the influence of each variable on the fire resistance given a specific intervention. Finally, this study ends by contrasting the algorithmic causal discovery with that obtained from domain knowledge and traditional machine learning. Our findings clearly show the potential and merit of adopting causality into our domain.
