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<p>Building simulation (BS) increasingly relies on data-driven models that extract patterns directly from measured data. However, these models often conflate statistical dependency with causal relationship. The idea of a causal lens introduces structural causal diagrams and do-operators to distinguish true causations from spurious associations. The “<i>causal lens</i>” perspective highlights how confounding bias can arise in observational modeling and emphasizes the importance of extracting true causality from building data. This suggests that BS move beyond pattern replication to enable counterfactual reasoning, thereby supporting reliable decision-making.</p>

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A causal lens for building data: What lies beyond the measured?

  • 期刊论文

  • 发表日期：

    2025年6月27日

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  • Jeeye Mun,
  • Cheol-Soo Park

摘要

Building simulation (BS) increasingly relies on data-driven models that extract patterns directly from measured data. However, these models often conflate statistical dependency with causal relationship. The idea of a causal lens introduces structural causal diagrams and do-operators to distinguish true causations from spurious associations. The “causal lens” perspective highlights how confounding bias can arise in observational modeling and emphasizes the importance of extracting true causality from building data. This suggests that BS move beyond pattern replication to enable counterfactual reasoning, thereby supporting reliable decision-making.