Introducing causal inference in the energy-efficient building design process

"What-if" questions are intuitively generated and commonly asked during the design process. Engineers and architects need to inherently conduct design decisions, progressing from one phase to another. They either use empirical domain experience, simulations, or data-driven methods to provide consequential feedback. We take an example from an interdisciplinary domain of energy-efficient building design to argue that the current methods for decision support have four limitations: 1. Less carefully inspected parametric independence raises the risks of biased results and spurious relationships. 2. The integration gap between data-driven methods and knowledge-based approaches. 3. Less explicit model interpretability for informed decision-making. 4. Ambiguous boundaries for machine assistance during the design process. In this study, we first clarify the nature of dynamic experience in individuals and constant principal knowledge in design. Sequentially, we introduce the causal inference into the energy-efficient design domain by proposing a four-step process to reveal and analyze the parametric dependencies within the design space by identifying the design causal diagram with interventions. The causal diagram provides a nexus for integrating domain knowledge with data-driven methods and allows interpretability and testability against the domain experience. The extraction of causal structures from the data is close to the nature design reasoning process. As an illustration, we applied the properties of the proposed estimators through simulations. The paper concludes with a feasibility study that demonstrates the realization of the proposed framework.