  "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 acquire 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 limitations or deficiencies in four aspects: parametric
independency identification, gaps in integrating knowledge-based and
data-driven approaches, less explicit model interpretation, and ambiguous
decision support boundaries. In this study, we first clarify the nature of
dynamic experience in individuals and constant principal knowledge in design.
Subsequently, we introduce causal inference into the domain. A four-step
process is proposed to discover and analyze parametric dependencies in a
mathematically rigorous and computationally efficient manner by identifying the
causal diagram with interventions. The causal diagram provides a nexus for
integrating domain knowledge with data-driven methods, providing
interpretability and testability against the domain experience within the
design space. Extracting 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 demonstrating the proposed framework's realization.
