TY - JOUR

T1 - Using causal inference to avoid fallouts in data-driven parametric analysis

T2 - A case study in the architecture, engineering, and construction industry

AU - Chen, Xia

AU - Sun, Ruiji

AU - Saluz, Ueli

AU - Schiavon, Stefano

AU - Geyer, Philipp

N1 - Funding Information: We gratefully acknowledge the German Research Foundation ( DFG ) support for funding the project under grant GE 1652/3-2 in the Researcher Unit FOR 2363 and under grant GE 1652/4-1 as a Heisenberg professorship.

PY - 2024/3

Y1 - 2024/3

N2 - The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. Recognizing the limitations of isolated methodologies - namely, the lack of domain understanding in data-driven models, the subjective nature of empirical knowledge, and the idealized assumptions in first-principles simulations, we explore their synergetic integration. We showed the potential risk of biased results when using data-driven models without causal analysis. Through a case study on energy consumption in building design, we demonstrate how causal analysis significantly enhances the modeling process, mitigating biases and spurious correlations. We concluded that: (a) Sole data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Integrating causal analysis results aid to first-principles simulation design and parameter checking to avoid cognitive biases. We advocate for the routine integration of causal inference within data-driven models in engineering practices, emphasizing its critical role in ensuring the models' reliability and real-world applicability.

AB - The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. Recognizing the limitations of isolated methodologies - namely, the lack of domain understanding in data-driven models, the subjective nature of empirical knowledge, and the idealized assumptions in first-principles simulations, we explore their synergetic integration. We showed the potential risk of biased results when using data-driven models without causal analysis. Through a case study on energy consumption in building design, we demonstrate how causal analysis significantly enhances the modeling process, mitigating biases and spurious correlations. We concluded that: (a) Sole data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Integrating causal analysis results aid to first-principles simulation design and parameter checking to avoid cognitive biases. We advocate for the routine integration of causal inference within data-driven models in engineering practices, emphasizing its critical role in ensuring the models' reliability and real-world applicability.

KW - Architecture, engineering, and construction industry

KW - Biased outcomes

KW - Building energy performance

KW - Causal inference

KW - Cognitive biases

KW - Data-driven models

KW - Domain knowledge

KW - Feature selection

KW - First-principles simulations

KW - Machine learning methods

UR - http://www.scopus.com/inward/record.url?scp=85180361755&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2309.1150

DO - 10.48550/arXiv.2309.1150

M3 - Article

AN - SCOPUS:85180361755

VL - 17

JO - Developments in the Built Environment

JF - Developments in the Built Environment

M1 - 100296

ER -