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

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

verfasst von

Xia Chen, Ruiji Sun, Ueli Saluz, Stefano Schiavon, Philipp Geyer

Abstract

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.

Organisationseinheit(en)

Institut für Entwerfen und Konstruieren

Externe Organisation(en)

University of California at Berkeley

Typ

Artikel

Journal

Developments in the Built Environment

Band

17

Anzahl der Seiten

12

Publikationsdatum

03.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Architektur, Tief- und Ingenieurbau, Bauwesen, Werkstoffwissenschaften (sonstige), Angewandte Informatik, Computergrafik und computergestütztes Design

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://doi.org/10.48550/arXiv.2309.1150 (Zugang: Offen)
https://doi.org/10.1016/j.dibe.2023.100296 (Zugang: Offen)