Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry

TL;DR

This paper demonstrates that integrating causal inference into data-driven models in architecture, engineering, and construction can prevent biased results and improve decision-making accuracy.

Contribution

It introduces a methodology for incorporating causal analysis into data-driven modeling, highlighting its importance in avoiding biases in building energy consumption studies.

Findings

Causal analysis helps identify biased or spurious correlations.

Feature selection should consider causal relationships to improve model validity.

Causal insights can guide simulation design and parameter validation.

Abstract

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. We investigated the synergetic pattern between the data-driven methods, empirical domain knowledge, and first-principles simulations. We showed the potential risk of biased results when using data-driven models without causal analysis. Using a case study assessing the implication of several design solutions on the energy consumption of a building, we proved the necessity of causal analysis during the data-driven modeling process. We concluded that: (a) 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) Causal analysis results can be used as an aid to first-principles simulation design and parameter checking to avoid cognitive biases. We proved the benefits of causal analysis when applied to data-driven models in building engineering.