Work

Causal Discovery and Validation in Summer Weather Data with a Conceptual Extension to Cooling Energy Use

Year: 2025

Type: article

Abstract: Traditional data-driven approaches emphasize input–output correlations and neglect dependencies among inputs, risking missed insights into key drivers of energy performance. Consequently, approaches that transcend correlation-centric analysis are warranted. Within this context, causal inference, which accounts for both statistical associations and temporal cause–effect relations, constitutes a promising direction. However, researchers cannot feasibly specify all causal relations relying solely on domain knowledge. Causal discovery is a data-driven methodology for analyzing causal relationships among variables, providing not only measures of association but also information on causal directionality. The authors employ two causal discovery algorithms—PC (Peter-Clark) and FCI (Fast Causal Inference)—on weather data. The discovered causal structures are compared, and two validation approaches are introduced to evaluate their statistical reliability; the authors also build on the identified causal structure to analyze the resulting causal pathways. The results show that both algorithms provide insights into causal relationships among variables, and the proposed validation approaches help establish the statistical reliability of the discovered structures. Moreover, the analysis of causal pathways indicates that causal effects can be identified and estimated with reliability.

Source: Buildings

DOI: 10.3390/buildings15234248

Authors: Han-Gyeong Chu, Hye-Gi Kim, Deuk-Woo Kim

Institution: Korea Institute of Civil Engineering and Building Technology

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,137