Human-building interaction through the lens of causality: A data-driven probabilistic causal learning approach

• This paper presents a data-driven probabilistic causal learning approach. • The approach enables the discovery of potential causal relationships from observed data. • A case study was conducted to demonstrate the potential of the proposed approach. • Causal occupant behavior models showed improved robustness compared to non-causal models. Understanding how people interact with buildings, i.e., human-building interaction, through the lens of causality is crucial for developing effective building solutions. Causal understanding enables accurate identification of where and how to intervene to improve building performance and occupant satisfaction, as well as estimation of the expected benefits. Despite its importance, causal reasoning to understand human-building interaction in the real world remains challenging due to (i) the difficulty in conducting large-scale controlled experiments and (ii) spurious correlations in observational data. In recent decades, data-driven causal reasoning methods have emerged, enabling further investigation of human-building interaction using observational data. However, existing methods are often inapplicable, as collecting quantitatively and qualitatively sufficient occupant data is difficult in real buildings. To address this, this paper presents a novel data-driven probabilistic causal learning approach involving two steps: (i) probabilistic causal discovery to infer potential causal structures and (ii) causal model training to develop causal models. A case study was conducted using the ecobee Donate Your Data dataset to demonstrate the potential of the proposed approach. We inferred potential causal factors of occupant setpoint adjustment behavior. Subsequently, we developed causal models and compared them with association-based models. Both models showed comparable predictive distributions where the test dataset distribution was similar to that of the training dataset. However, under data shift, the causal models showed better robustness. This suggests that the proposed approach has the potential to enable the development of causal models that may better explain underlying causal relationships and more reliable and robust occupant-centric solutions.