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Causality, Explanations, Machine Learning, and Engineering
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DOI：10.1007/s10699-025-10006-3.png)
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Abstract
En 中文
Causality and explanation are fundamental, yet often underacknowledged, in engineering. While engineering education emphasizes design principles, codes of practice, and technical considerations, the explicit notion of causality is typically buried under layers of empirical formulas, rule-based guidelines, and prescriptive procedures. This paper examines how causality remains elemental in engineering practices and why codal provisions and curricula often treat it only implicitly. This paper also explores the interplay between causality and machine learning (ML), which excels at detecting associations, but questions still arise regarding its ability to explain and capture causal relations. In a series of case studies, we argue that building codes conventionally distill collective engineering wisdom and embed causal reasoning in simplified yet codified rules. However, this embedding is seldom articulated as causality, which leads to potential gaps in how engineers explain their decisions. Thus, this paper advocates for a renewed focus on causality in engineering by bridging philosophical inquiry, engineering methodology, and ML.
Keywords:
Models
Machine learning
Philosophy of science
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