Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge. (arXiv:2204.05311v1 [cs.LG])

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.



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